results_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bf2b4f3f",
   "metadata": {},
   "source": [
    "# Table of Contents\n",
    "1. [Libraries](#libraries)\n",
    "2. [Settings](#settings)\n",
    "3. [Data](#data)\n",
    "4. [Selected Models Results](#selected-model-results)\n",
    "   1. [Table of Selected Model Results](#table-of-selected-model-results)\n",
    "   2. [Expert vs. Non-Expert](#expert-vs-non-expert)\n",
    "   3. [Human vs. Machine Generated Datasets](#human-vs-machine-generated-datasets)\n",
    "5. [Valid Response Rates](#valid-response-rates)\n",
    "   1. [Valid Response Rates Per Model](#valid-response-rates-per-model)\n",
    "   2. [Valid Response Rates Per Dataset](#valid-response-rates-per-dataset)\n",
    "6. [Properties Analysis](#properties-analysis)\n",
    "7. [All Model Results (Table 3)](#all-model-results)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "220d3fba-87cb-4cc2-b21c-698a470d5512",
   "metadata": {
    "tags": []
   },
   "source": [
    "# 1. Libraries <a id=\"libraries\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bfb15e5d-fe7f-4ffa-b1b6-6e1c497b52cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import pandas as pd\n",
    "import time\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import ttest_ind, spearmanr\n",
    "import colorcet as cc\n",
    "\n",
    "from results.utils.get_results_dataframes import json_to_df, plot_experts_vs_non_experts, plot_human_vs_machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "adf550f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ca434490",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.display.max_columns = None\n",
    "pd.options.display.max_rows = None\n",
    "\n",
    "pd.set_option(\"display.precision\", 2)\n",
    "palette = sns.color_palette(cc.glasbey_hv, n_colors=25)\n",
    "sns.set_theme(style=\"whitegrid\", palette=palette) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "736af066-f0cd-4cd0-b6e6-bc65c7cad512",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 2. Settings <a class=\"anchor\" id=\"settings\"></a>\n",
    "1- Path to files with evaluation results (e.g., `./results/<%d-%m-%Y>`),\n",
    "\n",
    "2- Get the `candidate models` `dataset names`, `model names`, and `name mappings (e.g., short names for models and task types` \n",
    "You can here change the list of models to be reported in the main paper in `results.name_mappings`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "81f0af46-f9bb-4530-88fa-234bc22a6603",
   "metadata": {},
   "outputs": [],
   "source": [
    "date = time.strftime(\"%d-%m-%Y\")\n",
    "results_path = f\"./results\"  # WARN: change to the correct path\n",
    "\n",
    "# WARN: assumes [cola] dataset has responses from all models\n",
    "model_names = list(reversed(list(json.load(open(f\"{results_path}/cola.json\")).keys())))\n",
    "model_names += list(\n",
    "    reversed(list(json.load(open(f\"{results_path}/wmt-23-zh-en.json\")).keys()))\n",
    ")\n",
    "model_names += list(\n",
    "    reversed(list(json.load(open(f\"{results_path}/wmt-23-en-de.json\")).keys()))\n",
    ")\n",
    "model_names = set(model_names)\n",
    "\n",
    "from results.utils.name_mappings import dataset_names, candidate_models, all_models, models_short_names, task_cabapility_mappings, tasks_dataset_names, dataset_human_vs_machines, subtask_to_cat"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a09c6f51-c405-47e1-a1b9-032cb215d67e",
   "metadata": {
    "tags": []
   },
   "source": [
    "# 3. Data <a class=\"anchor\" id=\"data\"></a>\n",
    "## `.json` to DataFrame and DataFrame to `.xlsx`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4f98c457-3565-407d-aa2a-c280816b1931",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "all datasets converted – use a spreadsheet app to open ./results/eval_results.xlsx!\n"
     ]
    }
   ],
   "source": [
    "xlsx_fpath = f'{results_path}/eval_results.xlsx'\n",
    "all_results = []\n",
    "with pd.ExcelWriter(xlsx_fpath) as writer:\n",
    "    for dataset in dataset_names:\n",
    "        # print(f'{dataset.upper()}: json to dataframe', end=' ')\n",
    "        df, dataset_results = json_to_df(f'{results_path}/{dataset}.json')\n",
    "        df.to_excel(writer, sheet_name=dataset)\n",
    "        all_results.extend(dataset_results)\n",
    "df_all_results = pd.DataFrame(all_results)\n",
    "print(f'\\nall datasets converted – use a spreadsheet app to open {xlsx_fpath}!')\n",
    "# remove gpt3 \n",
    "df_all_results = df_all_results[~df_all_results['model'].str.contains('gpt-3.5')]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0515a85",
   "metadata": {},
   "source": [
    "# 4. Selected Models Results <a class=\"anchor\" id=\"selected-model-results\"></a>\n",
    "Select results of the `candidate models` based on the highest valid reponse rate and performance\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4b010011",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_all_results['expert'] = df_all_results['expert'].replace({'true': 'Experts', 'false': 'Crowdsource', 'unknown': 'Mixed', 'uknown': 'Mixed'})\n",
    "df_all_results.rename(columns={'expert': 'Annotators'}, inplace=True)\n",
    "selected_df_all_results = df_all_results[df_all_results['model'].isin(candidate_models)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64e970e3",
   "metadata": {},
   "source": [
    "## 4.1 Table of Selected Model Results <a class=\"anchor\" id = \"table-of-selected-model-results\"></a>\n",
    "`Table 1` in the main paper: Scores per dataset for the models with >97 valid response rates\n",
    "Cohen's kappa for categorical annotations and Spearman's correlation for graded annotations. Datasets with both categorical and graded annotations appear twice. Datasets in \\colorbox{blue!25}{blue} concern human-generated language, while those in \\colorbox{red!25}{red} concern model-generated text.  `$\\sigma$' denotes the standard deviation of the scores across models per dataset (averaged over properties if more than one is judged per dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a1ffbfc2",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Type</th>\n",
       "      <th>Dataset (\\#Subtasks)</th>\n",
       "      <th>GPT-4o</th>\n",
       "      <th>Llama-3-70B</th>\n",
       "      <th>Mixtral-8x22B</th>\n",
       "      <th>Gemini-1.5</th>\n",
       "      <th>Mixtral-8x7B</th>\n",
       "      <th>Comm-R+</th>\n",
       "      <th>$\\sigma$</th>\n",
       "      <th>$\\alpha$</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{blue!25}cola (1)</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.16</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{blue!25}llmbar-natural (1)</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.13</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{blue!25}llmbar-adversarial (1)</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.25</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{blue!25}toxic-chat (2)</td>\n",
       "      <td>0.49 ±0.36</td>\n",
       "      <td>0.4 ±0.13</td>\n",
       "      <td>0.45 ±0.27</td>\n",
       "      <td>0.45 ±0.35</td>\n",
       "      <td>0.36 ±0.12</td>\n",
       "      <td>0.28 ±0.35</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{blue!25}cola-grammar (63)</td>\n",
       "      <td>0.47 ±0.22</td>\n",
       "      <td>0.29 ±0.24</td>\n",
       "      <td>0.28 ±0.23</td>\n",
       "      <td>0.26 ±0.24</td>\n",
       "      <td>0.21 ±0.18</td>\n",
       "      <td>0.13 ±0.14</td>\n",
       "      <td>0.13</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{red!25}topical-chat (2)</td>\n",
       "      <td>0.05 ±0.07</td>\n",
       "      <td>-0.02 ±0.02</td>\n",
       "      <td>-0.03 ±0.04</td>\n",
       "      <td>-0.03 ±0.04</td>\n",
       "      <td>0.02 ±0.03</td>\n",
       "      <td>0.01 ±0.02</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{red!25}roscoe-gsm8k (2)</td>\n",
       "      <td>0.59 ±0.35</td>\n",
       "      <td>0.65 ±0.27</td>\n",
       "      <td>0.62 ±0.38</td>\n",
       "      <td>0.6 ±0.24</td>\n",
       "      <td>0.58 ±0.36</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.23</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{red!25}roscoe-esnli (2)</td>\n",
       "      <td>0.29 ±0.06</td>\n",
       "      <td>0.31 ±0.15</td>\n",
       "      <td>0.13 ±0.13</td>\n",
       "      <td>0.11 ±0.18</td>\n",
       "      <td>0.1 ±0.11</td>\n",
       "      <td>0.03 ±0.05</td>\n",
       "      <td>0.17</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{red!25}dices-990 (1)</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{red!25}inferential-strategies (1)</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.06</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.16</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{red!25}roscoe-cosmos (2)</td>\n",
       "      <td>0.16 ±0.07</td>\n",
       "      <td>0.14 ±0.09</td>\n",
       "      <td>0.09 ±0.17</td>\n",
       "      <td>0.14 ±0.17</td>\n",
       "      <td>0.19 ±0.05</td>\n",
       "      <td>-0.03 ±0.01</td>\n",
       "      <td>0.13</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{red!25}qags (1)</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{red!25}medical-safety (2)</td>\n",
       "      <td>0.01 ±0.03</td>\n",
       "      <td>-0.02 ±0.03</td>\n",
       "      <td>-0.02 ±0.09</td>\n",
       "      <td>-0.03 ±0.08</td>\n",
       "      <td>0.0 ±0.06</td>\n",
       "      <td>0.01 ±0.02</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{red!25}dices-350-expert (1)</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.08</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{red!25}dices-350-crowdsourced (1)</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{red!25}persona-chat (2)</td>\n",
       "      <td>0.24 ±0.34</td>\n",
       "      <td>0.15 ±0.21</td>\n",
       "      <td>0.58 ±0.59</td>\n",
       "      <td>-0.03 ±0.04</td>\n",
       "      <td>0.54 ±0.65</td>\n",
       "      <td>0.48 ±0.74</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{red!25}roscoe-drop (2)</td>\n",
       "      <td>0.29 ±0.08</td>\n",
       "      <td>0.18 ±0.09</td>\n",
       "      <td>0.2 ±0.12</td>\n",
       "      <td>0.08 ±0.05</td>\n",
       "      <td>0.13 ±0.21</td>\n",
       "      <td>0.03 ±0.04</td>\n",
       "      <td>0.13</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{blue!25}dailydialog (1)</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{blue!25}switchboard (1)</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{red!25}persona-chat (4)</td>\n",
       "      <td>0.22 ±0.11</td>\n",
       "      <td>0.03 ±0.11</td>\n",
       "      <td>0.16 ±0.1</td>\n",
       "      <td>0.1 ±0.09</td>\n",
       "      <td>0.02 ±0.15</td>\n",
       "      <td>0.07 ±0.13</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{red!25}topical-chat (4)</td>\n",
       "      <td>0.26 ±0.03</td>\n",
       "      <td>0.21 ±0.15</td>\n",
       "      <td>0.13 ±0.04</td>\n",
       "      <td>0.17 ±0.12</td>\n",
       "      <td>0.21 ±0.18</td>\n",
       "      <td>0.14 ±0.05</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{red!25}recipe-crowd-sourcing-data (6)</td>\n",
       "      <td>0.78 ±0.05</td>\n",
       "      <td>0.75 ±0.05</td>\n",
       "      <td>0.6 ±0.15</td>\n",
       "      <td>0.67 ±0.09</td>\n",
       "      <td>0.57 ±0.24</td>\n",
       "      <td>0.32 ±0.28</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{red!25}roscoe-cosmos (2)</td>\n",
       "      <td>0.57 ±0.18</td>\n",
       "      <td>0.52 ±0.16</td>\n",
       "      <td>0.51 ±0.16</td>\n",
       "      <td>0.57 ±0.17</td>\n",
       "      <td>0.53 ±0.21</td>\n",
       "      <td>0.33 ±0.25</td>\n",
       "      <td>0.13</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{red!25}roscoe-drop (2)</td>\n",
       "      <td>0.57 ±0.22</td>\n",
       "      <td>0.5 ±0.17</td>\n",
       "      <td>0.44 ±0.15</td>\n",
       "      <td>0.44 ±0.13</td>\n",
       "      <td>0.32 ±0.12</td>\n",
       "      <td>0.21 ±0.22</td>\n",
       "      <td>0.13</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{red!25}roscoe-esnli (2)</td>\n",
       "      <td>0.49 ±0.24</td>\n",
       "      <td>0.38 ±0.21</td>\n",
       "      <td>0.38 ±0.17</td>\n",
       "      <td>0.35 ±0.21</td>\n",
       "      <td>0.32 ±0.12</td>\n",
       "      <td>0.09 ±0.08</td>\n",
       "      <td>0.17</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{red!25}roscoe-gsm8k (2)</td>\n",
       "      <td>0.82 ±0.12</td>\n",
       "      <td>0.77 ±0.17</td>\n",
       "      <td>0.81 ±0.14</td>\n",
       "      <td>0.81 ±0.12</td>\n",
       "      <td>0.79 ±0.13</td>\n",
       "      <td>0.68 ±0.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{red!25}newsroom (4)</td>\n",
       "      <td>0.59 ±0.02</td>\n",
       "      <td>0.63 ±0.01</td>\n",
       "      <td>0.44 ±0.05</td>\n",
       "      <td>0.55 ±0.03</td>\n",
       "      <td>0.5 ±0.07</td>\n",
       "      <td>0.36 ±0.06</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{red!25}summeval (4)</td>\n",
       "      <td>0.35 ±0.06</td>\n",
       "      <td>0.25 ±0.16</td>\n",
       "      <td>0.54 ±0.08</td>\n",
       "      <td>0.38 ±0.02</td>\n",
       "      <td>0.48 ±0.02</td>\n",
       "      <td>0.19 ±0.06</td>\n",
       "      <td>0.15</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{red!25}wmt-23-en-de (1)</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.04</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{red!25}wmt-23-zh-en (1)</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{red!25}wmt-human-en-de (1)</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{red!25}wmt-human-zh-en (1)</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Type                              Dataset (\\#Subtasks)      GPT-4o  \\\n",
       "0                             \\cellcolor{blue!25}cola (1)       0.34    \n",
       "1                   \\cellcolor{blue!25}llmbar-natural (1)       0.84    \n",
       "2               \\cellcolor{blue!25}llmbar-adversarial (1)       0.58    \n",
       "3                       \\cellcolor{blue!25}toxic-chat (2)  0.49 ±0.36   \n",
       "4                    \\cellcolor{blue!25}cola-grammar (63)  0.47 ±0.22   \n",
       "5                      \\cellcolor{red!25}topical-chat (2)  0.05 ±0.07   \n",
       "6                      \\cellcolor{red!25}roscoe-gsm8k (2)  0.59 ±0.35   \n",
       "7                      \\cellcolor{red!25}roscoe-esnli (2)  0.29 ±0.06   \n",
       "8                         \\cellcolor{red!25}dices-990 (1)      -0.24    \n",
       "9            \\cellcolor{red!25}inferential-strategies (1)       0.42    \n",
       "10                    \\cellcolor{red!25}roscoe-cosmos (2)  0.16 ±0.07   \n",
       "11                             \\cellcolor{red!25}qags (1)       0.72    \n",
       "12                   \\cellcolor{red!25}medical-safety (2)  0.01 ±0.03   \n",
       "13                 \\cellcolor{red!25}dices-350-expert (1)       -0.2    \n",
       "14           \\cellcolor{red!25}dices-350-crowdsourced (1)      -0.22    \n",
       "15                     \\cellcolor{red!25}persona-chat (2)  0.24 ±0.34   \n",
       "16                      \\cellcolor{red!25}roscoe-drop (2)  0.29 ±0.08   \n",
       "0                      \\cellcolor{blue!25}dailydialog (1)       0.69    \n",
       "1                      \\cellcolor{blue!25}switchboard (1)       0.66    \n",
       "2                      \\cellcolor{red!25}persona-chat (4)  0.22 ±0.11   \n",
       "3                      \\cellcolor{red!25}topical-chat (4)  0.26 ±0.03   \n",
       "4        \\cellcolor{red!25}recipe-crowd-sourcing-data (6)  0.78 ±0.05   \n",
       "5                     \\cellcolor{red!25}roscoe-cosmos (2)  0.57 ±0.18   \n",
       "6                       \\cellcolor{red!25}roscoe-drop (2)  0.57 ±0.22   \n",
       "7                      \\cellcolor{red!25}roscoe-esnli (2)  0.49 ±0.24   \n",
       "8                      \\cellcolor{red!25}roscoe-gsm8k (2)  0.82 ±0.12   \n",
       "9                          \\cellcolor{red!25}newsroom (4)  0.59 ±0.02   \n",
       "10                         \\cellcolor{red!25}summeval (4)  0.35 ±0.06   \n",
       "11                     \\cellcolor{red!25}wmt-23-en-de (1)       0.22    \n",
       "12                     \\cellcolor{red!25}wmt-23-zh-en (1)       0.17    \n",
       "13                  \\cellcolor{red!25}wmt-human-en-de (1)       0.63    \n",
       "14                  \\cellcolor{red!25}wmt-human-zh-en (1)       0.54    \n",
       "\n",
       "    Llama-3-70B Mixtral-8x22B   Gemini-1.5 Mixtral-8x7B      Comm-R+ $\\sigma$  \\\n",
       "0         0.56          0.54         0.45         0.55         0.12      0.16   \n",
       "1         0.72          0.72         0.79         0.54         0.56      0.13   \n",
       "2         0.41           0.2         0.29         0.06         0.11      0.25   \n",
       "3     0.4 ±0.13    0.45 ±0.27   0.45 ±0.35   0.36 ±0.12   0.28 ±0.35      0.1   \n",
       "4    0.29 ±0.24    0.28 ±0.23   0.26 ±0.24   0.21 ±0.18   0.13 ±0.14     0.13   \n",
       "5   -0.02 ±0.02   -0.03 ±0.04  -0.03 ±0.04   0.02 ±0.03   0.01 ±0.02     0.15   \n",
       "6    0.65 ±0.27    0.62 ±0.38    0.6 ±0.24   0.58 ±0.36          0.0     0.23   \n",
       "7    0.31 ±0.15    0.13 ±0.13   0.11 ±0.18    0.1 ±0.11   0.03 ±0.05     0.17   \n",
       "8        -0.16         -0.16        -0.12         -0.2        -0.09      0.05   \n",
       "9         0.08          0.02         0.22         0.06        -0.02      0.16   \n",
       "10   0.14 ±0.09    0.09 ±0.17   0.14 ±0.17   0.19 ±0.05  -0.03 ±0.01     0.13   \n",
       "11        0.69          0.66         0.65         0.68         0.13      0.21   \n",
       "12  -0.02 ±0.03   -0.02 ±0.09  -0.03 ±0.08    0.0 ±0.06   0.01 ±0.02     0.03   \n",
       "13        -0.2         -0.15        -0.03        -0.11         0.01      0.08   \n",
       "14       -0.15         -0.08        -0.02        -0.11        -0.08      0.06   \n",
       "15   0.15 ±0.21    0.58 ±0.59  -0.03 ±0.04   0.54 ±0.65   0.48 ±0.74     0.19   \n",
       "16   0.18 ±0.09     0.2 ±0.12   0.08 ±0.05   0.13 ±0.21   0.03 ±0.04     0.13   \n",
       "0          0.6          0.55         0.63         0.63         0.52      0.05   \n",
       "1          0.6          0.63         0.59         0.56         0.36      0.13   \n",
       "2    0.03 ±0.11     0.16 ±0.1    0.1 ±0.09   0.02 ±0.15   0.07 ±0.13     0.19   \n",
       "3    0.21 ±0.15    0.13 ±0.04   0.17 ±0.12   0.21 ±0.18   0.14 ±0.05     0.15   \n",
       "4    0.75 ±0.05     0.6 ±0.15   0.67 ±0.09   0.57 ±0.24   0.32 ±0.28      0.2   \n",
       "5    0.52 ±0.16    0.51 ±0.16   0.57 ±0.17   0.53 ±0.21   0.33 ±0.25     0.13   \n",
       "6     0.5 ±0.17    0.44 ±0.15   0.44 ±0.13   0.32 ±0.12   0.21 ±0.22     0.13   \n",
       "7    0.38 ±0.21    0.38 ±0.17   0.35 ±0.21   0.32 ±0.12   0.09 ±0.08     0.17   \n",
       "8    0.77 ±0.17    0.81 ±0.14   0.81 ±0.12   0.79 ±0.13    0.68 ±0.2     0.23   \n",
       "9    0.63 ±0.01    0.44 ±0.05   0.55 ±0.03    0.5 ±0.07   0.36 ±0.06      0.1   \n",
       "10   0.25 ±0.16    0.54 ±0.08   0.38 ±0.02   0.48 ±0.02   0.19 ±0.06     0.15   \n",
       "11        0.19          0.23         0.16         0.17         0.22      0.04   \n",
       "12        0.15          0.19         0.14         0.15         0.15      0.03   \n",
       "13        0.37          0.51         0.46          0.2         0.42       0.2   \n",
       "14        0.37          0.48         0.41         0.25         0.42      0.14   \n",
       "\n",
       "   $\\alpha$  \n",
       "0         -  \n",
       "1         -  \n",
       "2         -  \n",
       "3         -  \n",
       "4         -  \n",
       "5      0.08  \n",
       "6         -  \n",
       "7         -  \n",
       "8      0.14  \n",
       "9       1.0  \n",
       "10        -  \n",
       "11     0.49  \n",
       "12        -  \n",
       "13        -  \n",
       "14     0.16  \n",
       "15     0.33  \n",
       "16        -  \n",
       "0      0.59  \n",
       "1      0.57  \n",
       "2      0.33  \n",
       "3      0.08  \n",
       "4      0.41  \n",
       "5         -  \n",
       "6         -  \n",
       "7         -  \n",
       "8         -  \n",
       "9      0.11  \n",
       "10        -  \n",
       "11        -  \n",
       "12        -  \n",
       "13      0.5  \n",
       "14     0.09  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from results.utils.generate_tables import get_model_per_dataset_score\n",
    "categorical_models_per_dataset_score = get_model_per_dataset_score(\n",
    "    selected_df_all_results, type=\"categorical\"\n",
    ")\n",
    "graded_models_per_dataset_score = get_model_per_dataset_score(\n",
    "    selected_df_all_results, type=\"graded\"\n",
    ")\n",
    "# combine the two dataframes\n",
    "models_per_dataset_score = pd.concat(\n",
    "    [categorical_models_per_dataset_score, graded_models_per_dataset_score], axis=0\n",
    ")\n",
    "models_per_dataset_score = models_per_dataset_score.rename(\n",
    "    columns={\"Dataset (#Subtasks)\": \"Dataset (\\#Subtasks)\"}\n",
    ")\n",
    "models_per_dataset_score.to_latex(\n",
    "    f\"results/models_per_dataset_score.tex\", index=False, column_format=\"llllllll|cc\"\n",
    ")\n",
    "models_per_dataset_score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a9de9f8",
   "metadata": {},
   "source": [
    "## 4.2 Expert vs. Non-Expert <a class=\"anchor\" id=\"expert-vs-non-expert\"></a>\n",
    "\n",
    "Plotting the scores of the models across annotation types (i.e., expert vs crowd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "29ba7eb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "fig, axs = plt.subplots(1, figsize=(7, 5))\n",
    "ax_grad, grad_filtered_data = plot_experts_vs_non_experts(df_all_results[df_all_results['model']!= 'OLMo-7B-Instruct'], 'graded', ax=axs)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a97aa22",
   "metadata": {},
   "source": [
    "## 4.3 Human vs. Machine Generated Datasets <a class=\"anchor\" id=\"human-vs-machine-generated-datasets\"></a>\n",
    "`Figure 6`: Scores (Cohen's $\\kappa$ for categorical annotations and Spearman's correlation for graded annotations) on test items involving  human language vs.~machine-generated outputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d9a93f1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5jjK/cnRKnz59bPN5X/lAJmdITEzUzJkzba979OghHx8fxcXF6dKlS5IuT80yfPhwW5wJCQl5tmUYhkzTzLPwXdDvCQAAADjPlfn9/PnzNWDAgFz5/Y1cnQ9fmc926tRJjzzyiCTZplG8UsWKFeXn56fU1FSlpqba5ch79+7Ntf2tt95qyzVvvfVWRUREyMvLy26bnKkWpcvXBTl3k/7++++2keVX5vw3a/369frpp59sr3v37i3J/n0YMmSIbfm1Bh5dee1zdR7tqmsEACUbBXMAyIcnnnhCq1atUkxMjBITE/Xggw/qiSeeUL169ZSSkqKffvpJy5cv1+LFixUUFKSqVavq+PHjunjxoj744APVr19fixYt0sWLF/NsPzAw0DadyuLFi9WoUSOVK1dO9evXV58+fWxzEL7wwgsaOXKkatasqfPnz+v48ePavHmzOnfurNGjR6tjx462h+rs27dPr732mu6880598803ed4See+992r69OnKzMzUunXrNGPGDDVt2lQrV660Tcdy2223qUGDBoV+74KCgtS5c2f98MMPys7O1lNPPWV76OeRI0f0xx9/6I033rCbA3zNmjVq2bKlEhIS9Oabbxb62Nfy888/KzMzU4cOHdLixYsVHR0t6fJULDnzuFeqVEmlS5fWpUuXdPjwYX322WeqWLGi3n///WuOBA8MDNTFixcVFxenb775RlWqVFGlSpVUs2bNAn9PAAAAwHmuzu8HDhyoxx9/XLfffrsyMjIKNS/2lfnsjz/+qNWrV8tisejtt9/Ota3FYlHXrl313XffSZL+9a9/6emnn1ZsbKwWLVqUa/ucnHrz5s06efKkRo0apYEDB8rf318xMTHav3+/1q1bp2XLlqlatWrq1q2btmzZIkmaMWOGypQpIz8/P7uR2QV17tw5/fzzz0pISNCOHTv02Wef2dbddddd6tixY6734auvvlL16tV14sQJzZ49O892AwICbF9v3bpVrVu3lo+Pj+rXr1+k1wgAkIOCOQDkQ1BQkD788EONHDlSp06d0pkzZzR58uRrbv/ggw9q2rRpkmRL5sqXL69atWrZzRueo23btvrjjz8kydZumzZttHjxYvXq1Us//PCDVq5cqTNnzuiVV17JtX/OwzMDAgI0ZswY21zZixYt0qJFi+Tt7a3atWsrKirKbr/KlStr/PjxevXVV2W1WvXee+/Zrff399eUKVNuenqXl19+WQcPHtSZM2cUHR2tl156ybauTZs2ki7PuV6/fn0dOnRI0dHRCg8Pl3T54aLnzp27qeNfLa85HytVqqRZs2bZRulbLBYNHDhQS5YsUWZmpiZMmCDp8oj/ihUr5hlT27ZttWbNGmVnZ9tGkffv319Tp04t8PcEAAAAnOfq/D4uLk6vv/56ntuWKpW/0kloaKi6du2qH374QQkJCXr++eclXc5nT548mWv7f/zjH9qyZYuSk5P1xx9/2PLfmjVr2j3HKMcrr7yihx9+WGfOnNHmzZttDwHNy4ABA7Rs2TIdPHhQFy5csE2HUrNmzXz1JS9btmyxFeGv1LVrV02fPt32+q677lJwcLDi4+O1f/9+252aLVq0yHOUebNmzeTj46OMjAxFRkbaHkq6aNEitWrVqsiuEQAgB3OYA0A+3Xbbbfrmm280fvx4tWzZUkFBQfL29lblypV155136vXXX7fdRjls2DCNGTNGVatWla+vr9q0aaOFCxfaPUX+SuHh4XrooYcUEhKSZ3H69ddf1+uvv642bdqoXLly8vb2VpUqVdS+fXu99NJLtts9c449YcIEVatWTT4+PmrUqJHmzp2r2rVr27bx9fW1fT1kyBDNnz9fnTt3VlBQkEqVKqWQkBD169dPy5cvd8iDgapUqaIVK1bo73//u2rXrq3SpUvLz89Pt99+u+69915JkpeXlz744AN1795d5cqVU4UKFTR06NBrPuj0ZhiGodKlSys0NFRt2rTRv/71L33//fdq3ry53Xb//ve/FRYWpuDgYPn5+albt25asGCBypQpk2e7//3vf9WrVy9VqFAh17qCfk8AAADAua7M71u1aqWgoCB5eXmpbNmyatCggR566CF9+OGHGjFiRL7bnDZtmvr376/y5csrICBAffv21Zw5c/LctmbNmlq0aJFtRHVwcLCeeuopu8ElV8rJqZ988klbTu3v76/atWurX79+mj17tipXrixJ8vHx0fz589WnTx+VLVtWZcuWVa9evfIcvV4QFotF/v7+qlmzpnr27Kk5c+Zozpw5dg9SLVu2rObPn6927drJz89PoaGhevbZZ/Xss8/m2WaFChX03nvvqWHDhrny7KK8RgCAHIbJ0xEAwKOYppmr6J6RkaF77rlHp0+flmEY2rlzp+3BlQAAAAAAALiMKVkAwMOsWrVKv/76q3r27KkaNWooPj5eH330kU6fPi1J6tChA8VyAAAAAACAPLh9wfzo0aOaNGmS9uzZI39/f/Xt21djxoyRj4/PNffZtWuXhg4dmue6WrVqKSIiwlnhAoDTZWVlaenSpVq6dGmudcHBwXnOgQ4AwM0gJwcAAICncOuCeUJCgsLCwlSzZk3NnDlTsbGxmjp1qtLT020PZ8tLo0aN7J7mLEnJycl66qmn1LlzZ2eHDQBO1bBhQ917772KjIzU2bNnZbFYVL16dXXu3FlPPvmkKlas6OoQAQAehJwcAAAAnsStC+bLli1TSkqKZs2apaCgIElSdna2Jk6cqBEjRig0NDTP/cqWLatmzZrZLVu+fLmsVqv+9re/OTlqAHCuBg0aaMaMGa4OAwBQQpCTAwAAwJNYXB3AzdiyZYvat29vS8wlqVevXrJardq+fXuB2lq9erVq1qypJk2aODhKAAAAwHORkwMAAMCTuHXBPCoqSrVr17ZbFhAQoODgYEVFReW7nbNnz+rHH39kJAsAAABQQOTkAAAA8CRuPSVLYmKiAgICci0PDAxUQkJCvtv57rvvlJ2dfdPJ+Z49e2Sapry9vW+qHQAAAHiOzMxMGYah5s2buzoUpyAnBwAAQHFXkJzcrQvmjrJq1So1atRItWrVuql2TNOUaZrKyMhwUGQAAABAyUBODgAAgOLArQvmAQEBSkpKyrU8ISFBgYGB+Wrj5MmT2rdvn8aPH3/T8Xh7e8s0Td1222033RYAAAA8w5EjR2QYhqvDcBpycgAAABR3BcnJ3bpgXrt27VzzIiYlJSk+Pj7XPIrXsmrVKlksFvXu3dshMRmGIT8/P4e0BQAAAPfnycVyiZwcAAAAxV9BcnK3fuhn586dtWPHDiUmJtqWRUREyGKxqGPHjvlq49tvv1WbNm0UEhLirDABAAAAj0VODgAAAE/i1gXzwYMHy9/fX+Hh4dq2bZu++uorTZs2TYMHD1ZoaKhtu7CwMN1999259t+/f7+OHj160w8WAgAAAEoqcnIAAAB4ErcumAcGBmrhwoXy8vJSeHi43nzzTQ0cOFDjxo2z285qtSo7OzvX/qtWrZKPj4/uvffeogoZAAAA8Cjk5AAAAPAkhmmapquD8BSRkZGSpMaNG7s4EgAAABQX5IhFi/cbAAAAVytIjujWD/0EAAB5y87OVmZmpqvDADyet7e3vLy8XB0GCoHzJFB0OFcCANwJBXMAADyIaZo6c+aMLl686OpQgBIjKChIt9xyiwzDcHUoyAfOk4BrcK4EALgLCuYAAHiQnCJQSEiI/Pz8uCgFnMg0TaWmpiouLk6SVLlyZRdHhPzgPAkULc6VAAB3Q8EcAAAPkZ2dbSsCVaxY0dXhACWCr6+vJCkuLk4hISFMOVDMcZ4EXINzJQDAnVhcHQAAAHCMnLl4/fz8XBwJULLk/MwxH3bxx3kScB3OlQAAd0HBHAAAD8P0AkDR4mfO/fCZAUWPnzsAcC9W0+qWbTsCU7IAAAAAAAAAAGwshkVL9m5VbHKCQ9sNLRuoIU07ObRNR2OEOQAAKHbGjRun+vXrq379+tq1a5fdupkzZ9rWLV++3EURAoDrca4EAADOFJucoOjE8w795+gCvDNQMAcAoARw1S1vxf1WOwDI4crzFedKAACA4oMpWQAAKAGcdTvd9bjDrXYAkMMV50mJcyUAAEBxQ8EcAIASIud2Ok9Uv359SVL//v01depUSdKuXbs0dOhQSdKUKVP0wAMP6K+//lL37t0lSaNGjZLVatVnn30m0zQ1aNAgPffcc1qzZo3effddxcfHq1mzZvrf//6natWqSZISExM1ceJE7d+/X/Hx8UpLS1NQUJBatmypZ555RnXr1rXF1K1bN0VHR6tNmzYKCwvTu+++q5MnT6pmzZoaN26c2rdvX5RvEYB88OTzpMS5EgAAID8omAMAgBJp6dKlunjxou31Rx99pKioKG3atEmmaUqStm/frn/+859atmyZpMtFoNWrV9u1c/bsWa1Zs0a7du3Sd999p4oVK9qt379/v0aPHm1r8+DBg3r66ae1adMmBQUFOa+DAOAAnCsBAEBJwxzmAACgWBs6dKjtwXX169fXrFmzHNJuRkaGPv30U23cuFH+/v6SpI0bN2rgwIHavXu3evbsKUnas2ePYmNjJUmBgYGaOXOmNm/erMjISO3Zs0eTJk2SJF28eFGrVq3KdZzk5GSFh4fr559/1qhRoyRJqamp2rJli0P6AQAS50oAAABHoWAOAABKpO7du6tly5aqWrWq6tSpY1s+YsQIBQQEqEOHDrZlMTExkqRy5copOjpao0aNUtu2bdW8eXO99NJLtu2OHTuW6ziVKlVSeHi4ypUrpz59+tiWnz592hndAgCH4lwJAABKGqZkAQAAxdqiRYvUtm1b2+uZM2fma+Sk1Wq97vqqVavavi5Tpozt68qVK0uSvL29bcsyMjIkSQsWLLDN+5uXS5cu5VpWvXp1WSyXxyj4+PjkahMAHIFzJQAAgGMwwhwAALi9nILNlUWYv/7667r7lCqV97iBay2XpIiICElS6dKl9cUXX2j//v15Ti2QV2ySZBjGdbcFAGfiXAkAAHBjFMwBAIDbyxnp+NtvvykpKUkXLlzQokWLHH6cnJGOhmHI399fiYmJDpsnGACcjXMlAADAjTElCwAAcHs9e/bUBx98oJiYGHXs2FHZ2dl2oxUd5a677tIff/yh9PR09e7dW5J06623Ovw4AOAMnCsBAABujII5AAAlRGjZQI893tNPP62EhAStW7dOGRkZuvfee3XXXXdp7NixDj3OiBEjlJSUpNWrVystLU2dOnXS3//+dw0aNMihxwHgGkV9nizqY3KuBAAAuDHDNE3T1UF4isjISElS48aNXRwJAKAkSk9P17Fjx1SrVi27B7NJktW0ymIU/UxsrjouUJSu97MnkSMWteu938XxPOnqYwNF5UbnSgBA8fPW9tWKTjzv0DarBlTQcx3/5tA286MgOTlZGQAAJYCrCjEUgAC4C1eerzhXAgAAFB9kZgAAAAAAAAAAiII5AAAAAAAAAACSKJgDAAAAAAAAACCJgjkAAAAAAAAAAJIomAMAAAAAAAAAIMkDCuZHjx7V448/rmbNmqljx46aNm2aMjIy8rVvbGys/v3vf6tdu3Zq0qSJevXqpW+++cbJEQMAAACehZwcAAAAnqKUqwO4GQkJCQoLC1PNmjU1c+ZMxcbGaurUqUpPT9eECROuu29cXJweeugh1apVS6+++qrKli2rP//8M9+JPQAAAABycgAASjqraZXFcM6YXGe2DVyLWxfMly1bppSUFM2aNUtBQUGSpOzsbE2cOFEjRoxQaGjoNfd94403dMstt+ijjz6Sl5eXJKl9+/ZFETYAAADgMcjJAQAo2SyGRUv2blVscoJD2w0tG6ghTTs5tE0gP9z6TzRbtmxR+/btbYm5JPXq1UtWq1Xbt2+/5n7Jycn6/vvv9cgjj9gScwAAAAAFR04OAABikxMUnXjeof8cXYAH8sutC+ZRUVGqXbu23bKAgAAFBwcrKirqmvv98ccfyszMVKlSpfToo4+qUaNG6tixo9544w1lZmY6O2wAAHAD48aNU/369W3/ri66/fXXX2rQoIFt/YMPPujwGHbt2mVrf/ny5dfddvny5bZtd+3a5fBYCuLUqVOaNGmSevXqpebNm6tZs2a6++67NWrUKH3xxRceN9XFX3/9pZkzZ2rmzJk6cOCAQ9suyPdASUZO7jqcKwuPc6XjcK4EAHgat56SJTExUQEBAbmWBwYGKiHh2n+FOnv2rCTppZde0oMPPqjRo0dr3759mjFjhiwWi55//vlCx2SaplJTUwu9PwAAhXXp0iVZrVZlZ2crOzvbbp3FMGRYiv7v5KbVKqtpFny/q/b54osv1K5dO7vXV25jmmauPt8sq9Vq9/X12i/Its4UERGhF198Uenp6XbLT548qZMnT2rjxo3q0KGDbrnlFpfE5wynTp3SrFmzJEmVK1dWvXr1HNZ2fj/X7OxsWa1WpaWl2e2TwzRNGYbhsLiKG3fKyYvjeVLiXFnUOFcWz3Ml4Gmc/bv/6t8BrmIYhnx9fZ16jLS0tGLT35LCEz/XguTkbl0wL6ycX84dOnTQuHHjJEnt2rVTSkqKPv74Y4WHh6tMmTKFajszM9Phf7EHACC/SpUqpUuXLtkts1gs8vX1VcL6pcq+EFdksXiVD1Fgj4eVUYgL46svtjds2KDY2FgFBgYqOztbK1assFtvtVpzFT5u1pWjCzMzM6/bfq9evdSrVy/ba0fHkh+HDh3Sv//9b2VmZspisejvf/+7+vXrp4oVK+r8+fP69ddf9fXXXysjI8Ml8eVXenp6gfKwgnxOBZXfti9duqSsrKzrjqb28fFxWFyewlU5eXE6T0qcK4sa58rifa4EPIW3t7caNmqkUk6aciwrO1v7//+dWq7m6+urhg0bOvUYx44dU1pamlOPAXue+rnmNyd364J5QECAkpKSci1PSEhQYGDgdfeTZDf6Qrr8gKE5c+boxIkTql+/fqFi8vb21m233VaofQEAuBmXLl1STEyMSpcunedFdPaFOGWdjS7yuApTKLxyPuPKlSvr9OnTWrt2rR577DFt3rxZsbGxtuXS5WJXmTJltHXrVi1atEhHjhzRxYsXZZqmqlatqnvvvVfDhw+3e19SUlI0b948rV27VtHR0fL29latWrX09NNPq0uXLnZxG4ahuXPn6quvvlJGRobat2+vl19+2TZn84oVK/Sf//xHkrRgwQK1adNGP/30k4YNGyZJmjBhgk6cOKHVq1crMzMz1/6SFB8fr9mzZ2vLli2Ki4tTuXLl1L59ez3zzDOqUaPGDd+zefPm2S6awsLCNGbMGNu6cuXKqUaNGurfv7/dPhkZGVqwYIFWr16tU6dOycvLS40bN9bw4cPtHrwYFham3bt3q0qVKpo6daqmT5+uQ4cOqUqVKgoPD1fv3r3t2o2IiNAnn3yiQ4cOKTMzU7Vq1dJDDz2khx56yDaqY9asWXr//fclSfPnz9eCBQv0008/qX379po1a5beeust7dy5UzExMUpKSpKvr68aNGigYcOG6a677pIkvfjii1q5cqXtuK+88opeeeUVSdK6detUtWpVJScna+7cuVq/fr1iYmLk6+urFi1aaPTo0XYXAenp6Zo2bZq+++47Wa1W3XPPPerWrZttvbe393WLU6VKldKtt96q0qVL51p35MiRa+7nCdwpJy+u50mJc6XEubKknysBT2IYhkp5eTn1QZh169YtFqOui+Iuulq1ahWLvpYknvi5FiQnd+uCee3atXP9dTopKUnx8fG55lG80o0K2lePOCkIwzDk5+dX6P0BACgsi8Uii8UiLy+vYvUAvcLEcmWCNmDAAM2aNUvLly/XsGHD9OWXX0qSBg4cqJkzZ9q29/LyUmRkZK45fI8dO6Y5c+YoOjpa06dPl3S5APTYY4/p4MGDtu0uXbqkyMhI/fHHH+rWrZssV0zNMGPGDJ07d872es2aNfL29tabb74pSXbb5nwGVy57++237QqKV+8fGxurBx98ULGxsbZtLly4oO+++07bt2/XZ599plq1al3z/crOztaOHTtsr5988skbvu/Z2dl6+umn7faTLs9F+9NPP+mtt96yFXdyPo/z58/rySeftBWbjh07phdeeEENGzZUnTp1JF0u7uR8LjkOHTqk//3vfzpy5IhefvnlXO/Z2LFjdfHiRduxvLy89P333ys6+v8Kl0lJSdq9e7d+/vlnzZs3Tx07drxuIm+xWJSenq5HH31Uhw8fti3PzMzUDz/8oB07dmj+/Plq1aqVJGnSpEl2c+8uX75cW7dutWvvWu9pzuft6+ubZ6HIk6djkdwrJy+u50mJc6XEubKknysBT5TzIExncPZ0GcVJSeprSVLUn2tBcnK3fuhn586dtWPHDiUmJtqWRUREyGKxqGPHjtfcr2rVqqpXr16upGfHjh0qU6YMI8QBAChG7r77bgUFBenQoUPatGmTNm/eLIvFogEDBuTatkuXLlq2bJl27typP/74Q9u2bVOXLl0kSatXr7YVGhYuXGgrALVr104RERH69ddftWDBgjxvPczKytKnn36q7du32+Z9XbNmTb6nT7BYLNfdf8aMGYqNjVW5cuX0ySefKDIyUitWrFBQUJASEhL09ttvX7f9ixcv2uZrLlu2rIKDg23rGjdubPdQwJxC2OrVq2250Msvv6zffvtNW7ZsUfPmzWWapiZPnpxruof09HT169dPP/30k/73v/9JujzFw9q1ayVdfqhczkjIBx54QDt37tQvv/yiIUOGSJI+/fRTu4JMjoCAAH311Vf67bff9M9//lPS5YcZrlmzRr/++qsiIyP15ZdfytfXV6ZpasmSJZKkqVOnatGiRbZ2pkyZokOHDunQoUOqVq2aFi5cqMOHD8vLy0vvvfeeIiMjtWbNGtWoUUMZGRmaMmWKJOn48eO20Zd16tTR+vXrtXHjRpUvX/667zsuIycvHjhXcq7kXAl3YDWdN3+9M9sGULK49QjzwYMHa/HixQoPD9eIESMUGxuradOmafDgwQoNDbVtFxYWppiYGK1bt862bOzYsXr66af12muvqWvXroqMjNTHH3+sJ598khHiAAAUI97e3rr//vu1aNEi/fvf/1ZWVpY6deqkypUr59o2NDRU77zzjnbu3KmzZ8/azetomqZOnDihoKAgbdmyxbZ82rRptrzhylvrrzRw4EC1bNlS0uXi4OHDh5WZmamzZ88qJCTkhn240f6bN2+WdHlk4KOPPppr/127dt3wGDnyO3Liyvdg4sSJmjhxot36+Ph4HTlyxG5KDC8vL40bN05ly5ZV3759NWHCBEmyTfewfft2W+Fo+fLldqMQr+zL1Q+bGzNmjO644w5Jso1ILl26tCZMmKCDBw8qMTHR7nbNY8eOFaiP2dnZCg8Pz7X+999/V3Jysn777TdbQe7RRx9V9erVJV3OIXOmj8C1kZMXD5wrOVdyroQ7sBgWp05TAjib1bTKYjhn/LEz20bBuHXBPDAwUAsXLtSrr76q8PBw+fv7a+DAgRo7dqzddnk9qbtbt25666239P7772vp0qUKCQnRM888o+HDhxdlFwAAQD4MGjRIixYtUkJCgu311axWq0aOHKn9+/dfs52cB5GdP3/51tiyZcvaFfSupWbNmravr5x39coHnd3M/hcuXLju/jmjPZcvX67x48fbrVu0aJFatWolPz8/paamKikpSefOnVPFihUlSZGRkdq1a5eGDh1qt1/Oe3A9Oe93jooVK6ps2bLX7Edh2pSkBg0a2L3et2+fwsPDr/kgq/xO1ZHfeOLi/u8hj1d+P+TnewPk5MUJ58qLkjhX5uBcieLKmdOUAM7GH31KBrcumEuXbwVbsGDBdbdZvHhxnst79+6d68ErAACg+KlXr56aNWum3377TRUrVrR7wFiOEydO2ApAHTp00PTp01WxYkW9/vrr+vjjj+22rVChgk6cOKHk5GTFxcXdcORjqVL/lzIVZj7qG+1fvnx5xcfHq1atWoqIiMi1/kYPw/Hy8lKHDh20fv16SZdznysfZJeXChUq2L7etm2b3dQEOce8OlZvb+/r9uPKNt966y3dd999N2xTUq6Hv61fv95WAJowYYIGDRokHx8ftW3b1lYQu14cV8Zz4sQJ+fn56aeffrKL/8p4rvz8r5wb+cqvcX3k5MUD50rOlZwrAcD5+KOP52OcPwAAcAvDhw9X9+7dNXr06FwX85L9CEYfHx+VLl1akZGR+vrrr3NtmzNXryT9+9//1vHjx5WSkqJdu3Zpw4YNzunAdXTu3FnS5dvnZ86cqYSEBKWlpWnPnj2aOHGiPvzwQ0mX57rNmXc251/btm0lSaNGjbK9Lx9++KHmzZunc+fOKSMjQ3/99VeuY3bq9H8jWCZMmKDo6GhlZGTo6NGjmjNnjp577rkC96Njx462h73NmDFDkZGRysjI0JkzZ/Tll1+qX79++Wrnys/S399fWVlZ+vDDD3MVgKTLc/rmOHLkiN0I5pw+pqam6n//+5/Onj2rS5cuaf/+/XrzzTf12muvSZKaN29ue7DeJ598olOnTik6OloLFy4sUP+B4oBzJedKzpUAANwctx9hDgAASobu3bure/fu11xfu3Zt1ahRQydOnNAPP/xgmwf31ltv1blz5+y2HTp0qCIiInTw4EHt2LFD9957r23d6NGjr3scZ/jHP/6hbdu2KTY2VrNmzdKsWbPs1o8ePfqGbdxxxx2aPHmyXnzxRWVmZmratGmaNm3aNbfv06ePVq5cqZ07d2rjxo3auHGj3fo2bdoUuB/VqlXT008/rZkzZ+r48eMaOHBggduQpK5du2r+/PmSLhfp/v3vfysoKEgBAQF2D5aUpBo1aigwMFAJCQmaN2+e5s2bp9DQUG3ZskVhYWGKiIjQ4cOH9fnnn+vzzz+327d///62Nvr166fly5fr6NGj6tGjhyQpKCioUPEDrsS58vo4V3KuBADgRiiYAwBQQniVv/ED19z5eN7e3po9e7YmTpyoyMhIBQYGKiwsTMnJybmKKv7+/vr000/14Ycfau3atTp16pS8vb1Vu3ZtNW7cuEjjli7P//rVV1/p/fff1+bNmxUXF6eyZcuqatWq6tSpU75HG95///1q1KiRFi5cqB07dthukw8ODlaDBg3UpUsX263/Xl5e+uCDD7RgwQKtWrVKJ06cUKlSpXTLLbeoVatW6tu3b6H6Mnr0aNWpU0effPKJDhw4oKysLIWEhKhhw4a6++6789VGu3btNGnSJH3wwQeKjY3V7bffrgkTJuiZZ57JVQTy9fXVtGnTNH36dB0/ftxuLt+yZctq6dKlmjNnjtatW6fo6Gj5+vqqcuXKat++va0IJEkvv/yySpcurW+//VZWq1Xdu3fX3Xffna8CHDxHUZ+3XHFMzpWcKzlXAgBwfYZ5o4nekG+RkZGS5JLkEQCA9PR0HTt2TLVq1VKZMmXs1plWqwxL0c/E5qrjAkXpej97EjliUbve+10cz5OuPjZQVG50rkTJ8db21Q6f/7lqQAU91/FvDm3TEejrzaGvrudJfS1ITk5WBgBACeCqQgwFIADuwpXnK86VAAAAxQeZGQAAAAAAAAAAomAOAAAAAAAAAIAkHvoJAAAAuK2kpCStWrVKJ0+eVGJioq5+PJFhGJo8ebKLogMAAADcDwVzAAAAwA39+OOPGj16tFJSUq67HQVzAAAAIP8omAMA4GGuHmEKwLlc9TM3ZcoUJScnX3cbwzCKKBr3wnkSKHr83AEA3AUFcwAAPIS3t7ckKTU1Vb6+vi6OBig5UlNTJf3fz2BRiYqKkmEYql+/voYPH67y5cvLy8urSGNwN5wnAddx1bkSAICComAOAICH8PLyUlBQkOLi4iRJfn5+jC4FnMg0TaWmpiouLk5BQUFFXqy+5ZZb9Ndff2nMmDHq2rVrkR7bXXGeBIqeq8+VAAAUFAVzAAA8yC233CJJtmIQAOcLCgqy/ewVpSFDhmjq1Knas2cPBfMC4DwJuIarzpUAABQUBXMAADyIYRiqXLmyQkJClJmZ6epwAI/n7e3tstGSAQEBql69uj744AMdPXpUrVu3VmBgYK7t+vXrV/TBFWOcJ4Gi58pzJQAABUXBHAAAD+Tl5cWFKeDhXnzxRRmGIdM0tWHDBm3YsCHXNoZhUDC/Bs6TAAAAyAsFcwAAABQrVtMqi2Fxu7ZdwTRNu/8BAAAA3BwK5gAAAChWLIZFS/ZuVWxygkPbDS0bqCFNOzm0TVcaPXq0q0MAAAAAPA4FcwAAABQ7sckJik487+owijUK5gAAAIDjUTAHAAAA3NzBgwd1/PhxSVLNmjXVoEED1wYEAAAAuCkK5gAAAICb+v333zV+/HgdOXLEbvltt92mqVOnqlGjRi6KDAAAAHBPnvPEIwAAAKAEOXHihMLCwnTkyBGZpmn3788//1RYWJhOnjzp6jABAAAAt0LBHAAAAHBDc+bMUUpKikzTVKVKldS5c2d16dJFwcHBkqSUlBTNnj3bxVECAAAA7oUpWQAAAAA3tHPnThmGoZ49e+qNN95QqVKXU/usrCz961//0vfff68dO3a4OEoAAADAvTDCHAAAAHBDZ8+elST179/fViyXpFKlSql///6SpHPnzrkkNgAAAMBdUTAHAAAA3FDZsmUlSXv37s21LmdZzjYAAAAA8sftp2Q5evSoJk2apD179sjf3199+/bVmDFj5OPjc939unXrpujo6FzL9+3bp9KlSzsrXAAAAMAhmjZtqs2bN2vOnDk6cuSImjZtKulyPrtu3ToZhmFb5mzk5AAAAPAUbl0wT0hIUFhYmGrWrKmZM2cqNjZWU6dOVXp6uiZMmHDD/e+991498cQTdstulNQDAAAAxcGwYcO0ZcsWWa1WrV27VmvXrrWtM01TFotFjz/+uNPjICcHAACAJ3HrgvmyZcuUkpKiWbNmKSgoSJKUnZ2tiRMnasSIEQoNDb3u/pUqVVKzZs2cHygAAADgYO3bt9dLL72kqVOnKjMz025dqVKl9OKLL6pdu3ZOj4OcHAAAAJ7Erecw37Jli9q3b29LzCWpV69eslqt2r59u+sCAwAAAIrAkCFDtG7dOo0bN06DBw/W4MGDNW7cOK1fv16PPPJIkcRATg4AAABP4tYjzKOiojRgwAC7ZQEBAQoODlZUVNQN91+1apU+//xzeXt7q1WrVvrnP/+p+vXrOytcAAAAwOFuueUWDRs2zGXHJycHAACAJ3HrgnliYqICAgJyLQ8MDFRCQsJ19+3WrZuaNGmiKlWq6NSpU5ozZ44eeeQRrVy5UtWrVy90TKZpKjU1tdD7AwAAlGSGYcjX19epx0hLS5Npmk49xpVM05RhGDfdzu7duyVJt99+u8qWLWt7fSOtW7e+6WNfDzk5ACA/PPF3/LXQV8eir0XPE/takJzcrQvmN+Oll16yfd2qVSt17NhRvXr10rx58/TKK68Uut3MzEwdOHDAARECAACUPL6+vmrYsKFTj3Hs2DGlpaU59RhXc8RDLB977DFZLBZ98sknatGihR577LEbJv2GYWj//v03fWxnIScHgJLDU3/H54W+OhZ9LXqe2tf85uRuXTAPCAhQUlJSruUJCQkKDAwsUFshISFq2bKl/vjjj5uKydvbW7fddttNtQEAAFBSOWIk9o3UqlWrSEezHDlyxGFtXR13cRiBRE4OAMgPT/wdfy301bHoa9HzxL4WJCd364J57dq1c82LmJSUpPj4eNWuXdslMRmGIT8/P5ccGwAAADfm7NtLr+aoC45+/frJMAxVqlTJ7rWrkZMDAIqLov4d70r01TPRV+cpSN7s1gXzzp07a86cOXbzJkZERMhisahjx44Fais2Nla//PKL+vbt64xQAQAAgJsyderU6752FXJyAAAAeBK3LpgPHjxYixcvVnh4uEaMGKHY2FhNmzZNgwcPVmhoqG27sLAwxcTEaN26dZKk1atXa9OmTerSpYtCQkJ06tQpffDBB/Ly8tLjjz/uqu4AAAAA+TZ+/HgZhqGRI0fq1ltvtVt3/vx5bdmyRdLlkejORE4OAAAAT+LWBfPAwEAtXLhQr776qsLDw+Xv76+BAwdq7NixdttZrVZlZ2fbXlerVk1xcXGaPHmykpKSVK5cObVr107PPvusqlevXtTdAAAAAApsxYoVMgxDAwcOzFUwP3bsmMaNGyeLxeL0gjk5OQAAADyJWxfMJalOnTpasGDBdbdZvHix3etmzZrlWgYAAAB4ipSUFElF91BQcnIAAAB4CrcvmAMAAAAlxfr167Vhwwa7ZXPnzlWFChVsr03T1C+//CJJ8vf3L9L4AAAAAHdHwRwAAABwEwcPHrRNxSJdLo7nzFV+NcMw1KBBg6IMDwAAAHB7FMwBAAAAN2Oapl3RPC+VKlXSuHHjijIsAAAA4LrK+ZSRabXKsFicdoybbZ+COQAAcFtW0yqL4ZxEy5ltA4XVv39/tWnTRqZpKiwsTIZh6KWXXlK9evVs21gsFgUEBKhOnTry8vJyYbQAAACAPV9vHxkWixLWL1X2hTiHt+9VPkSBPR6+qTYomAMAALdlMSxasnerYpMTHNpuaNlADWnayaFtAo5QtWpVVa1aVZLUunVrSVLbtm1Vt25dV4YFAAAAFEj2hThlnY12dRh5omAOAADcWmxygqITz7s6DKDILV682NUhAAAAAB6HgjkAAADgps6fP68vv/xSv//+uxITE2W1Wu3WG4ahhQsXuig6AAAAwP1QMAcAAADcUHR0tB566CGdO3cuz/VXPhgU8GQ8zwIAADgSBXMAAADADc2aNUtnz57Ncx2FcpQkPM8CAAA4EgVzAAAAwA3t2rVLhmFo2LBhmj9/vgzD0JtvvinTNDV58mTVrFlTkyZNcnWYQJHgeRYAAMBRuLcMAAAAcENxcXGSpI4dO9qWhYaG6r777tNzzz2nX3/9VZ999pmrwgMAAADcEgVzAAAAwA35+PhIksqUKaMyZcpIujyvuSQFBgbKNE2tWrXKZfEBAAAA7ogpWQAAAAA3VL58eaWlpSklJUWVK1fWsWPHNH36dB08eFBr166VJGVmZro4SgAAAMC93FTBfN++ffrmm28UFRWltLQ0LViwQN9//70kqUePHipbtqxDggQAAABgr27duoqJiVFcXJy6du2qY8eOKT4+XvPnz5d0+cGfbdq0cXGUAAAAgHspdMH8zTff1EcffSRJMk1ThmGodOnSmjdvno4cOSLTNNW/f3+HBQoAAPLHalplMZwz65oz2wZQMAMHDlRoaKjKly+vkSNH6scff9SBAwds6+vXr6///ve/LowQAAAAcD+FKph/8803+vDDD/Nc161bN/35559as2YNBXMAAFzAYli0ZO9WxSYnOLTd0LKBGtK0k0PbBFB4PXr0UI8ePWyvv/rqK/3666+KjY1VlSpV1LRpU1ks/IELgHtiAAAAwFUKVTD/5JNPJEm1a9fW3/72N82YMcO2rk6dOpKko0ePOiA8AABQGLHJCYpOPO/qMAAUIYvFolatWrk6DABwCAYAAABcpVAF8z///FOGYWjs2LGqUKGC3brg4GBJUnx8/M1HBwAAAEmMtIM0a9asQu03evRoB0cCAEWDAQAAAFe4qYd+5nWLZ2xs7OWGS91U0wAAALgCI+0wa9YsGYZR4P0omAMAAAD5V6iqdq1atXTgwAF9+OGHevzxx23Lo6Oj9dFHH8kwDNvULAAAAHAMRtrBNM0CbV+YAjsAAABQkhWqYN6nTx/t379fe/fu1ZgxY2yJ+JUPHbr//vsdEyEAAAAATZkyxdUhAAAAAB6vUAXzxx57TJs3b9aPP/4o6f9GruSMeOnQoYMefvhhB4UIAAAAoH///q4OAQAAAPB4hSqYlypVSh999JEWLlyoVatW6fjx45KkmjVrqk+fPho6dGie85sDAAAAcLz9+/fr6NGjSktL04MPPujqcAAAAAC3VeCCeXZ2tu3BngMHDtSTTz7p8KAAAAAA3FhkZKRefPFFHTlyRNLlOz/79u2rTp06KTk5WfPnz1fbtm1dHCUAAADgPgo8DNxqtap79+7q3r271q5d64yYAAAAANzA0aNHFRYWpiNHjsg0Tdu/0qVLq3v37rJarYqIiHB1mAAAAIBbKXDB3NvbWxUrVpQkValSxeEBAQAAALixWbNmKTU1VRaLRc2aNbNb17RpU0nSL7/84oLIAAAAUBDlfMrItFqdegxnt+9JCjWH+d/+9jctWLBAW7ZsUceOHR0dU4EcPXpUkyZN0p49e+Tv76++fftqzJgx8vHxyXcbCxYs0JQpU9S1a1fNnTvXidECAAAAjrFr1y4ZhqHnnntOzZo105AhQ2zrqlatKkm2qRSdjZwcAACg8Hy9fWRYLEpYv1TZF+Ic3r5X+RAF9njY4e16qkIVzDt06KANGzZo0aJFunDhgrp27apKlSrJMAy77Vq3bu2QIK8lISFBYWFhqlmzpmbOnKnY2FhNnTpV6enpmjBhQr7aiI+P13vvvWcbNQ8AAAC4g6SkJElSw4YNc63LysqSJKWlpTk9DnJyAAAAx8i+EKess9GuDqPEK1TBfPjw4TIMQ6ZpatWqVVq1alWubQzD0P79+286wOtZtmyZUlJSNGvWLAUFBUm6/FDSiRMnasSIEQoNDb1hG2+88Ya6deummJgYp8YKAAAAOFJwcLBOnz6tbdu2qVu3bnbrcuYuv+WWW5weBzk5AAAAPEmB5zDPYZqm7f9r/XO2LVu2qH379rbEXJJ69eolq9Wq7du333D/n3/+WevXr9fzzz/vxCgBAAAAx+vQoYNM09THH3+s1157zbZ86NCh+vrrr2UYRpFMn0hODgAAAE9SqBHm/fv3d3QchRIVFaUBAwbYLQsICFBwcLCioqKuu292drZeffVVjRw5UiEhIc4MEwAAAHC4kSNHau3atUpMTNSBAwds0yPu3r1b0uW8ePjw4U6Pg5wcAAAAnqRQBfMpU6Y4Oo5CSUxMVEBAQK7lgYGBSkhIuO6+n376qdLS0jRs2DCHxmSaplJTUx3aJgAA+WUYhnx9fZ16jLS0tCK5k+xG6Ktj0VfnMU0z17N+HKFatWqaP3++xo0bpz///NNuXd26dfX666+rcuXKDj/u1cjJ4UqeeM4An6unKkmfK311jHI+ZWRarTIshZ4g44ZMq1Xply7l670sis/V2fL7feOJfS1ITl6ogvmVEhMTdeLECUlSjRo18kyWi5tz585pxowZev311+Xj4+PQtjMzM3XgwAGHtgkAQH75+vrm+QBARzp27FiRPEjwRuirY9FX53J0zpmjUaNGWrVqlQ4ePKhjx45JkmrVqqUGDRo45XiORE4OR/DUc0ZJx+fqmUrS50pfHdS2t48Mi0UJ65cq+0Kcw9v3Kh+iwB4P5/u9LIrP1dlKel/zm3MWumAeHR2tiRMnatu2bbZqvWEY6tSpkyZMmKCqVasWtul8CwgIUFJSUq7lCQkJCgwMvOZ+7777rurXr69WrVopMTFRkpSVlaWsrCwlJibKz89PpUoV7q3x9vbWbbfdVqh9AQC4Wc4YxXq1WrVqFZvRLM5GX4ueJ/b1yJEjDm8zLS1NI0aMkCQNGjRIffr0cVmRnJwcruSJ5wzwuXqqkvS5FkVfa9eu7dS+5rftouhr9oU4ZZ2Ndlr7+f2+KYq+OltJ7mtBcvJCZaDx8fF66KGHdO7cuVxD27ds2aKHH35Yy5cvV6VKlQrTfL7Vrl0717yISUlJio+PV+3ata+537Fjx7R79261bt0617rWrVvrww8/VOfOnQsVk2EY8vPzK9S+AAC4A3e/Na8g6KtnKuq+OuOCw9fXV5GRkUpPT9fIkSMd3n5BkJPD05Wk82NJwufqmUrC55ozTUmZMmWcdgxnT4NS3JSE75scJbmvBcnJC1UwnzNnjs6ePSvp8lD26tWryzAMnTx5UhkZGYqPj9ecOXP00ksvFab5fOvcubPmzJljN29iRESELBaLOnbseM39XnzxRdsolhyTJ09WmTJl9Nxzz6l+/fpOjRsAAAC4Wc2aNdOPP/6omJgYl8ZBTg4AQNEpqmlKgJKsUAXzzZs3yzAMtW3bVm+//bbKly8vSbpw4YKee+457dy5Uz/88IPTC+aDBw/W4sWLFR4erhEjRig2NlbTpk3T4MGDFRoaatsuLCxMMTExWrdunSTp9ttvz9VWQECA/Pz81LZtW6fGDAAAADjC+PHjNXToUL3zzjuqWrWq2rdv75I4yMkBACh6zp6mBCjJClUwj42NlSQNGzbMViyXpPLlyyssLEw7d+60beNMgYGBWrhwoV599VWFh4fL399fAwcO1NixY+22s1qtys7Odno8AAAAQFEZNWqUrFarzp49qyeeeEKlS5dWhQoV7G43NQxD69evd2oc5OQAAADwJIUqmJcpU0bJyck6fvx4rnU5y5w5l9KV6tSpowULFlx3m8WLF9+wnfxsAwAAABQX0dHRMgzDViBPT0/X6dOnbetN0yyyBzaRkwMAAMBTFKpgfvvtt+unn37SO++8o/j4eDVp0kSStG/fPi1ZskSGYeR5iyUAAAAAxzFN87qvAQDFn9W0ymI45wGLzmwbADxVoQrmjzzyiH766SddunRJH3/8sd26nJEsQ4YMcUiAAAAAAHI7ePCgq0MAADiAxbBoyd6tik1OcGi7oWUDNaRpJ4e2CQAlQaEK5j179tSTTz6pefPm5bn+73//u+69996bCgwAAABA3tLS0my5eKtWrdSuXTsXRwSgKDAS2XPFJicoOvG8q8MAAKiQBXNJ+te//qV77rlHq1evts1bXrNmTf3tb39T06ZNHRUfAAAAgKv4+vpq7ty5ysrK0nvvvefqcAAUEUYiAwDgfIUumEtS06ZNKY4DAAAALlC7dm0dPnxYWVlZrg4FQBFiJDIAAM5VqPutDh48qJUrV+rbb7/NtW716tVauXIlcyoCAAAATjR69GhJ0rx585SUlOTiaAAAAADPUKgR5m+99Za2bt2qQYMG6b777rNb98svv2jZsmXq0qWL5syZ45AgAQAAANjbuHGjqlatqr1796pr165q0aKFKlWqZLeNYRiaPHmyiyIEAAAA3E+hCub79++XJHXqlHuOs44dO2rp0qX6/fffby4yAAAAANe0YsUKGYYhwzCUkpKibdu25bkdBXMAAAAg/wpVML948aIkyWLJPaOLYRh22wAAAABwDtM08/w6R05uDgAAACB/ClUwL1++vM6ePauvv/5a3bt3t1v39ddfS5KCgoJuOjgAAAAAeVu0aJGrQwAAAAA8TqEK5i1atNCaNWu0bt06PfLII+rataskafPmzfr1119lGIZatGjhyDgBAAAAXKFNmzauDgEAAADwOIUqmD/++ONav369rFar9uzZoz179tjWmaYpLy8vPfHEEw4LEgAAAEDeYmNjtWbNGh0/flySVLNmTd17770KDQ11bWAAAACAGypUwbxZs2b63//+p4kTJyojI8NunY+Pj/773/+qWbNmjogPAAAAwDUsW7ZMkydPVmZmpt3y6dOn6z//+Y8eeughF0UGAAAAuKdCFcwlacCAAbrzzjsVERFhN5qlZ8+ejGYBAAAAnGznzp2aOHGipNwP/MzIyNDEiRNVo0YNtWvXzhXhAQBQZMr5lJFptcqwWJx2DGe3D6D4KHTBXJJCQ0MVFhbmqFgAAHAaq2mVxXBOguvMtgHgWubPny/TNGWxWHT33XerSZMmMgxDe/fu1fr162Wapj7++GMK5gAAj+fr7SPDYlHC+qXKvhDn8Pa9yocosMfDDm8XQPF0UwXzHMeOHdOBAwdktVrVuHFj1ahRwxHNAgDgMBbDoiV7tyo2OcGh7YaWDdSQpp0c2iYA5MfevXtlGIZGjRqlZ555xm7dzJkz9d5772nv3r0uig4AgKKXfSFOWWejXR0GADeX74L5tm3btGbNGhmGof/85z8qXbq0JOn111/XwoUL7W4DfeCBBzRp0iQZhuH4iAEAKKTY5ARFJ553dRgA4BApKSmSpKZNm+Zal7MsZxsAAAAA+ZPv+8e//fZbffnll4qOjrYVy7du3ar58+fLarXKNE3bv+XLl2vZsmVOCxoAAAAo6SpVqiRJWrFihbKzs23LrVarVqxYYbcNAAAAgPzJ9wjz/fv3S5LuvPNO27Lly5dLkgzDUMWKFdW8eXNt3rxZGRkZ+vrrr/Xww8zvBAAAADhD+/bttWLFCkVEROjnn39Wo0aNJF3O2+Pj42UYhtq3b+/iKAEAAAD3ku8R5vHx8ZKkW2+91bZs165dtq9nzZqlmTNn2uZPPHLkiKNiBAAAAHCVUaNGyd/fX5J09uxZbd68WZs3b1Z8fLxM05S/v79GjRrl4igBAAAA95LvgnliYqIkyc/PT5J08uRJnT9/XoZhKDg4WM2aNZMkNW7cWJJ06dIlB4cKAAAAIMett96q+fPnq3bt2nbTI5qmqTp16ujjjz+2G+wCAAAA4MbyPSWLv7+/EhMT9csvv6h9+/Zat26dbV3r1q1tX6elpUmSypcv78AwAQAAAFytSZMm+vbbb3XgwAEdO3ZMklSrVi3dfvvtLo4MAAAAcE/5Lpg3atRIO3bs0OzZs7Vp0yYdOnTItq579+62r3PmOg8NDXVgmAAAAACu5fbbb6dIDgAAADhAvqdkCQsLkyRZrVbt379f2dnZki7fCnplwTwiIkKGYahFixYODhUAAAAo2SIjI9W/f3898MADioqKynObo0eP2raJjIws4ggBAAAA95bvgnmXLl00ZcoUhYSEyDRNSVKzZs303nvvqXTp0pKkrVu36s8//5RpmrrzzjudEzEAAABQQi1fvlwHDhxQSEiIateunec2derUUUhIiA4cOKCvvvqqiCMEAAAA3Fu+p2SRpP79+6t///46d+6c/Pz85Ovra7f+zjvv1B9//CFJ8vLyclyU13H06FFNmjRJe/bskb+/v/r27asxY8bIx8fnuvv985//1L59+xQXFydvb2/Vq1dPo0aNotAPAACAYuvnn3+WYRi69957r7tdz549tXnzZu3evbtI4iInBwAAgKcoUME8R8WKFfNcbhhGkRXKJSkhIUFhYWGqWbOmZs6cqdjYWE2dOlXp6emaMGHCdffNzMzUsGHDVLNmTV26dElffvmlhg8frkWLFqlVq1ZF1AMAAAAg/2JiYiRJVapUue52OevPnDnj9JjIyQEAAOBJClUwLy6WLVumlJQUzZo1S0FBQZKk7OxsTZw4USNGjLjug0ffffddu9edO3dW9+7d9fXXX5OcAwAAoFiyWq2SpPPnz193u5z1Ods7Ezk5AAAAPEm+5zAvjrZs2aL27dvbEnNJ6tWrl6xWq7Zv316gtry8vFSuXDllZmY6OEoAAADAMW655RZJl+cyv56c9ZUrV3Z6TOTkAAAA8CRuXTCPiorK9bCjgIAABQcHKyoq6ob7m6aprKwsXbhwQfPmzdOJEyf00EMPOStcAAAA4Ka0a9dOpmlq27ZtGjt2rKKjo+3Wx8TE6Pnnn9fWrVtlGIbatWvn9JjIyQEAAOBJ3HpKlsTERAUEBORaHhgYqISEhBvu/+WXX+qll16SJPn5+entt99W8+bNbyom0zSVmpp6U20AABzLMIxcD6p2tLS0NJmm6dRj5Ad9dSz6WvQ8sa+macowDIe0NXToUH3xxRfKzs5WRESE1q5dq9DQUIWEhCguLk6xsbG2aVi8vLz02GOPOeS410NODlfyxHPGtdBXxypJfU1PT3dqX/PbdlH01dny+31DX90Lfc3NE/takJzcrQvmN6t79+5q0KCBLly4oIiICI0ZM0azZs1Sly5dCt1mZmamDhw44MAoAQA3y9fXVw0bNnTqMY4dO6a0tDSnHiM/6Ktj0dei56l99fHxcUg7tWrV0rhx4zRp0iQZhqHs7GzFxMTo9OnTuS5+xo8fr1q1ajnkuM5ETo6b4annjLzQV8cqCX0t51NGptWqMmXKOKV9SbJmZ+n3P/bnayqtovhcnS2/3zf01b3Q19w8ta/5zcndumAeEBCgpKSkXMsTEhIUGBh4w/0rVKigChUqSLr8gKGEhAS98cYbN5Wce3t767bbbiv0/gAAx3PUyM7rqVWrVrEZpeRs9LXo0VfHKuq+HjlyxKHtPfroo6pUqZKmTZummJgYSfaj+6pUqaIXXnhBPXv2dOhxr4WcHK7kieeMa6GvjlUS+urr7SPDYlHC+qXKvhDn8Pa9yocosMfDqlu3br5HrLq7/H7f0Ff3Ql9z88S+FiQnL3TBPCMjQ+vWrdPvv/+uxMRE262fOQzD0OTJkwvbfL7Url0717yISUlJio+PzzWPYn40atRIW7ZsuamYDMOQn5/fTbUBAHA/7n67WkHQV89EX53HGRccPXv21N133609e/bo0KFDSkpKUrly5VS/fn01b95cXl5eDj/mtZCTw9NxfvRMJamv2RfilHU2+sYbFlJJei/pq2eir57p6r4WJCcvVMH8woULeuyxx3T06NE81+fMCePsgnnnzp01Z84cu3kTIyIiZLFY1LFjxwK398svv6h69eqODhMAAABwOC8vL7Vq1UqtWrVyaRzk5AAAAPAkhSqYv/fee9ccxl6UQ/YHDx6sxYsXKzw8XCNGjFBsbKymTZumwYMHKzQ01LZdWFiYYmJitG7dOknSDz/8oJUrV6pr166qXLmyEhIStHr1am3btk1vvfVWkcUPAAAAuDtycgAAAHiSQhXMt27dKsMw1LdvX61cuVKGYWjcuHG6dOmSZs+erYYNG+rZZ591dKy5BAYGauHChXr11VcVHh4uf39/DRw4UGPHjrXbzmq1Kjs72/a6evXqysjI0JtvvqkLFy6ofPnyql+/vhYvXqw2bdo4PW4AAADAU5CTAwAAwJMUqmB++vRpSVKvXr20cuVKSVLjxo3VokULlSlTRlOmTNGePXvUtm1bhwV6LXXq1NGCBQuuu83ixYtz7fP+++87MSoAAACg5CAnBwAAgKewFGannIcI+fv7y8fHR5IUHx8vSapRo4ZM09SyZcscFCIAAAAAAAAAAM5XqBHmQUFBOnPmjFJTUxUSEqLo6GjNmDFDZ8+e1VdffSVJSkpKcmigAADHs5pWWYxC/e3UpW0DAAAAAAA4Q6EK5rVr19aZM2d07tw5dejQQZ9//rmioqI0adIkSZcf/NmkSROHBgoAcDyLYdGSvVsVm5zg0HZDywZqSNNODm0TAAAAuFnlfMrItFplWJw3sMPZ7QMAnKtQBfNevXrJMAxJ0tNPP63NmzcrNjbWtj44OFgvvfSSYyIEADhVbHKCohPPuzoMAEAhJCUladWqVTp58qQSExNlmqbdesMwNHnyZBdFBwDFj6+3jwyLRQnrlyr7QpzD2/cqH6LAHg87vF0AQNEpVMF84MCBGjhwoO31d999p3Xr1ikuLk5VqlTRXXfdJX9/f4cFCQAAAMDejz/+qNGjRyslJeW621EwB4Dcsi/EKetstKvDAAAUQ4UqmF/N399f/fr1c0RTAAAAAPJhypQpSk5Ovu42OXeFAgAAAMifQhfMExISNGfOHG3YsEHR0Zf/Klu1alV1795dw4cPV/ny5R0WJAAAAAB7UVFRMgxD9evXt+XfXl5erg4LAAAAcGuFKpifOnVKjz32mG3e8py5Ek+dOqUFCxbou+++0yeffKLq1as7LlIAAAAANrfccov++usvjRkzRl27dnV1OAAAAIBHKNRjm1977TWdOXNGpmnaPVgo53VsbKxee+01hwUJAAAAwN6QIUNkmqb27Nnj6lAAAAAAj1GoEea7du2SYRiqXr26Jk6cqMaNG8swDO3du1cTJ07UiRMntGvXLkfHCgAAAOD/CwgIUPXq1fXBBx/o6NGjat26tQIDA3Ntx7OGAAAAgPwrVMG8TJkySk9P1z//+U+1b9/etrxDhw567rnn9I9//EN+fn4OCxIAAACAvRdffFGGYcg0TW3YsEEbNmzItY1hGBTMAQAAgAIo1JQsPXv2lCSlpqbmWpeWliZJuu+++24iLAAAAAA3kjM9Ys7UiHn9AwAAAJB/hRph/vDDD+vHH3/UtGnTlJ2drSZNmkiS9u3bp7feeksNGjTQQw89pJiYGLv9qlSpcvMRAwAAANDo0aNdHQIAAADgcQpVMO/bt6+kyyNZXnrpJbt1pmnq/Pnz+tvf/ma33DAM7d+/v5BhAgAAALgSBXMAAADA8QpVMDdNU4Zh2L7Oaz0AAAAAAAAAAO6kUAXz1q1bOzoOAAAAAAUUFRWlBQsW6Pfff1dSUpKsVqvdesMwtH79ehdFBwAAALifQhXMFy9e7Og4ABRzVtMqi1Go5wS7tG0AADzVoUOHNHjwYKWnp9vu8Lz6LtCc1wAAAADyp1AF8ytlZ2crLi5OkhQSEiIvL6+bDgpA8WMxLFqyd6tikxMc2m5o2UANadrJoW0CAFASzJ49W2lpabbXhmHYFcqZJhEAAAAouEIXzE+cOKE333xTW7Zs0aVLlyRJpUuXVufOnTV27FjVqlXLYUECKB5ikxMUnXje1WEAAABJv/zyiwzD0PPPP6/p06dLkj755BOVKlVKL7zwgoKDgzVnzhwXRwkAAAC4l0LNgbB//34NHDhQ69ats90Capqm0tPTtW7dOg0cOFD79+93dKwAAAAA/r8LFy5Ikho1amS3vFmzZhozZox++eUXTZ482RWhAQAAAG6rUAXzyZMnKykpyXabZ2BgoAIDAyVdni8xJSVFU6ZMcVyUAFCErKb1xhsVw7YBACWLr6+vJKlUqVK2r48ePSrp/+Yw37hxo2uCAwAAANxUoaZkiYyMlGEYuuOOOzR9+nTVqFFD0uVpWv71r39p37592rdvn0MDBYCiwnztAAB3UKFCBSUnJyslJUXVq1fX4cOHNW3aNO3YsUM//vijJPF8IQAAAKCAClUwDwwMVHx8vEaNGmUrlktSjRo1NHLkSD399NMqX768w4IEgKLGfO0AgOKufv36OnHihKKjo3XPPffo8OHDSk1N1dq1ayVdfvBnly5dXBwlAHdQzqeMTKtVhqVQN6Hni7PbBwDAUQpVMH/ggQc0Z84cnT59Ote6nGWDBg26ucgAAAAAXNPQoUN1xx136LbbblPTpk31xx9/aNOmTbb1Xbt21YsvvujCCAG4C19vHxkWixLWL1X2hTiHt+9VPkSBPR52eLsAADhDoQrmbdq00Zo1azR9+nSdO3dOTZo0kSTt27dPCxYsUIMGDdSiRQvt3r3bbr/WrVvffMQAAAAA1KpVK7Vq1cr2evbs2Tp9+rRiY2NVpUoVhYSEuDA6AO4o+0Kcss5GuzoMAABcqlAF8yeeeEKGYcg0Tc2ePdtunWmaOnTokJ544gm75YZhaP/+/YWPFAAAAMB1Va5cWZUrV3Z1GAAAAIDbKlTBXLpcGL/y/7zWFYWjR49q0qRJ2rNnj/z9/dW3b1+NGTNGPj4+19wnLi5OCxYs0Pbt23Xy5EmVK1dOrVu31nPPPaeqVasWWewAAADAzUhKStKqVat08uRJJSYm5srDDcPQ5MmTnR4HOTkAAAA8RaEK5v3793d0HIWSkJCgsLAw1axZUzNnzlRsbKymTp2q9PR0TZgw4Zr7/fHHH1q3bp0GDBigpk2b6sKFC5o9e7YGDRqk1atXq0KFCkXYCwAAAKDgfvzxR40ePVopKSnX3c7ZBXNycgAAAHiSQhXMp0yZ4ug4CmXZsmVKSUnRrFmzFBQUJEnKzs7WxIkTNWLECIWGhua5X8uWLfX999+rVKn/636LFi3UtWtXrVy5Mtd0MgAAAEBxM2XKFCUnJ193G8MwnB4HOTkAAAA8SaGnZCkOtmzZovbt29sSc0nq1auXXn75ZW3fvl0PPPBAnvsFBATkWnbLLbeoQoUKiotz/BPBAQAAAEeLioqSYRhq0aKFhg4dqvLly7skDnJyAAAAeJJCF8zPnz+vL7/8Ur///rsSExNltVrt1huGoYULF950gNcTFRWlAQMG2C0LCAhQcHCwoqKiCtTWsWPHdO7cOdWpU8eRIQIAAABOceuttyoqKkrDhw9Xly5dXBYHOTkAAAA8SaEK5tHR0XrooYd07ty5PNebplkkt38mJibmOTIlMDBQCQkJ+W7HNE1NmjRJISEhuu+++24qJtM0lZqaelNtAMWNYRjy9fV16jHS0tKK9IHB10JfHYu+Fj366lj0teh5Yl+dlRs/88wzGjNmjL788ku1bt1afn5+Dj9GfpCTw5U88ZxxLUXRV2fL73tJX90Lfc2NvroX+pqbJ/a1IDl5oQrms2bN0tmzZ/NcVxSFckebOXOmfvzxR3300Uc3faGRmZmpAwcOOCgyoHjw9fVVw4YNnXqMY8eOKS0tzanHyA/66lj0tejRV8eir0XPU/vq4+Pj8DZ79uypUaNGafbs2dq2bZtq1aqlsmXL2m1TFHd9Ogo5OQrDU88ZeSmKvjpbft9L+upe6Gtu9NW90NfcPLWv+c3JC1Uw37VrlwzD0LBhwzR//nwZhqE333xTpmlq8uTJqlmzpiZNmlSYpgskICBASUlJuZYnJCQoMDAwX218/vnneu+99/Taa6+pffv2Nx2Tt7e3brvttptuByhOiuIPYbVq1So2I3ecjb4WPfrqWPS16NFXxyrqvh45csQp7a5Zs0Zz586VYRhKS0vLVSAuqrs+ycnhSp54zrgWdxycdrX8vpf01b3Q19zoq3uhr7l5Yl8LkpMXqmCe8xCejh07av78+ZKk0NBQtWzZUunp6XrppZf02Wefady4cYVpPt9q166da17EpKQkxcfHq3bt2jfcf926dXrllVf07LPPauDAgQ6JyTAMl90OC7gzd7/VpyDoq2eir56Jvnqmou6rsy443nnnHbvnCLmqoEdODk9Xks6PzlaS3kv66pnoq2eir57p6r4WJCcvVMHcx8dHaWlpKlOmjMqUKaNLly4pOjpaLVu2VGBgoEzT1KpVq5xeMO/cubPmzJljN29iRESELBaLOnbseN19d+3apeeee06DBg1SeHi4U+MsaaymVRbD4nZtAwAAuJPTp0/LMAz17t1bjz/+uAIDA2WxFH2eRE4OAAAAT1Kognn58uWVlpamlJQUVa5cWceOHdP06dN18OBBrV27VtLleQOdbfDgwVq8eLHCw8M1YsQIxcbGatq0aRo8eLBCQ0Nt24WFhSkmJkbr1q2TJB09elTh4eGqWbOm+vbtq99++822bYUKFXTrrbc6PXZPZjEsWrJ3q2KT8/+Qp/wILRuoIU07ObRNAAAAd9WoUSP9+uuv6tOnj+644w6XxUFODgAAAE9SqIJ53bp1FRMTo7i4OHXt2lXHjh1TfHy8bXoWwzDUpk0bhwaal8DAQC1cuFCvvvqqwsPD5e/vr4EDB2rs2LF221mtVmVnZ9te7927V0lJSUpKStLDDz9st23//v01depUp8fu6WKTExSdeN7VYQAAAHisCRMmKCwsTHPnzlWdOnVUvXp1l8RBTg4AAABPUqiC+cCBAxUaGqry5ctr5MiR+vHHH+0eMlS/fn3997//dViQ11OnTh0tWLDgutssXrzY7vUDDzygBx54wIlRAQAAAM719NNPyzRN/fbbb7rnnnsUEBCgsmXL2m1jGIbWr1/v9FjIyQEAAOApClUw79Gjh3r06GF7/dVXX+nXX39VbGysqlSpoqZNmyolJcVhQQIAAACwFx0dbXt4kWmaSkxMVGJiom29aZpOe+AoAAAA4Kny/VSgTZs2XbsRi0WtWrXSfffdp+bNm+vChQt67LHHHBIgAAAAgLyZpinTNO2+vnIZAAAAgILJ9wjzZ555RtOmTVPv3r2vu92ZM2cUFhamkydP3nRwAAAAgKOU8ykj02qVYcn3mJECc3b7Vzp48GCRHAcAAAAoSfJdMM/KytK//vUvpaWlacCAAXluc/z4cT3xxBOKiYnh9k8AAAAUK77ePjIsFiWsX6rsC3EOb9+rfIgCezx84w0dIC0tTfPmzZMktWrVSu3atSuS4wIliaf9kQ0AAORPvgvmFotFVqtVL730klJTU3NNuXLo0CE9+eSTOnfunCQpNDTUsZECAAAADpB9IU5ZZ6NdHcZN8fX11dy5c5WVlaX33nvP1eEAHsmT/sgGAADyL98F86lTp+rFF19UVlaWJk+erJSUFI0cOVKS9Ntvv2nEiBFKTEyUaZq69dZbNX/+fKcFDQAAAJR0tWvX1uHDh5WVleXqUACP5gl/ZAMAAPmX73u/7r//fr3zzjvy8fGRaZp699139eabb2rnzp16/PHHbcXyunXrasmSJapataoz4wYAAABKtNGjR0uS5s2bp6SkJBdHAwAAAHiGfI8wl6QePXpo9uzZGj16tNLS0vTRRx9p3rx5slqtkqQ77rhDH330kYKCgpwRKwAAAID/b+PGjapatar27t2rrl27qkWLFqpUqZLdNoZhaPLkyS6KEAAAAHA/BSqYS1LHjh310UcfaeTIkUpKSpJpmjIMQ61bt9acOXPk5+fnjDgBAAAAXGHFihUyDEOGYSglJUXbtm3LczsK5gAAAED+5btgPn78eLvXNWvWVGRkpKTLI1cqVaqkV1991bae0SwAAACAc5mmmefXOQzDKMpwAAAAALeX74J5zgiWK+W8Nk1T33//fa59KJgDADxdOZ8yMq1WGZZ8PxakwJzdPko2vofd16JFi1wdAgAAAOBxCjQlS16jVq6F0SwAgJLA19tHhsWihPVLlX0hzuHte5UPUWCPhx3eLpCD72H31aZNG1eHAAAAAHicfBfMR48e7cw4AABwa9kX4pR1NtrVYQCFxvewe0tJSVFSUpKsVmuudVWqVHFBRAAAAIB7omAOAAAAuKmvv/5as2fP1okTJ/JcbxiG9u/fX8RRAQAAAO6rQFOyAAAAACge1q9fr3//+98yDKNAUycCAAAAuDaevgQAcLichwg6k7PbB4DibvHixZKk8uXLS7o8mrxevXoKDAyUJNWqVUutWrVyWXwAAACAO2KEOQDA4XiIIAA438GDB2UYhl544QWNGzdOkvTKK6/o9ttv1+jRo3XgwAHNmDHDxVECAAAA7oWCOXATrKZVFsM5N2o4s22gqPAQQbirnLskDIvzzsPObh+eLyUlRZJUtWpVGYYhScrMzJSvr6+GDh2qESNG6LXXXtOCBQtcGCUAAADgXiiYAzfBYli0ZO9WxSYnOLTd0LKBGtK0k0PbBADkH3dJwB2ULVtWCQkJys7OVrly5ZSUlKTt27erbdu2OnTokCRp7969Lo4SAAAAcC8UzIGbFJucoOjE864OAwDgBNwlgeIsNDRUCQkJSk5OVr169fTzzz/rww8/1BdffKGLFy/KMAxVqFDB1WECAAAAboX7gAEAAAA31LBhQ5mmqePHj2vgwIG25RcvXpRpmjJNUw8++KALIwQAAADcDyPMAQBAvjCvN1C8jBkzRoMHD1alSpVUtWpVXbx4UZ988oliY2NVpUoVPfTQQxo2bJirw4QH4ncBAADwZBTMAQBAvjCvN1C8hIaGKjQ01PZ62LBhFMhRJPhdAAAAPBkFcwAAUCDM6w0UP/v379fRo0eVnp6uQYMGuToclAD8LgAAAJ6KgjkAAADgpiIjI/Xiiy/qyJEjkiTDMHT//ferU6dOSk5O1vz589W2bVsXRwkAAAC4D7efGO7o0aN6/PHH1axZM3Xs2FHTpk1TRkbGDfdbsmSJRowYoXbt2ql+/fqKiIgogmgBAAAAxzh69KjCwsJ05MgR20M+TdNU6dKl1b17d1mt1iLLccnJAQAA4CncumCekJCgsLAwZWZmaubMmRo7dqw+//xzTZ069Yb7fv3117pw4YK6dOlSBJECAAAAjjVr1iylpqbKYrGoWbNmduuaNm0qSfrll1+cHgc5OQAAADyJW0/JsmzZMqWkpGjWrFkKCgqSJGVnZ2vixIkaMWKE3UOQ8trXYrHor7/+0sqVK4smYAAAAMBBdu3aJcMw9Nxzz6lZs2YaMmSIbV3VqlU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",
      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(15, 5))\n",
    "ax_cat, cat_filtered_data = plot_human_vs_machine(df_all_results, 'categorical', ax=axs[0])\n",
    "ax_grad, grad_filtered_data = plot_human_vs_machine(df_all_results, 'graded', ax=axs[1])\n",
    "plt.tight_layout()\n",
    "# Save the plot transparently\n",
    "plt.savefig(f'./results/human_vs_machine_generated.pdf', bbox_inches='tight', pad_inches=0.1, dpi=300, transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71c2d080",
   "metadata": {},
   "source": [
    "# 5. Valid Response Rates <a class=\"anchor\" id=\"valid-response-rates\"></a>\n",
    "## 5.1 Valid Response Rates Per Model <a class=\"anchor\" id = \"valid-response-rates-per-model\"></a>\n",
    "`Figure 4`: Plotting the response ratio per model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1f3d9790",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_response_scores = df_all_results.groupby('model')['response_ratio'].mean().sort_values(ascending=True)\n",
    "df_all_results['model'] = pd.Categorical(df_all_results['model'], categories=model_response_scores.index, ordered=True)\n",
    "response_ratio = df_all_results.groupby(['model'])['response_ratio'].mean().reset_index()\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "plt.figure(figsize=(6, 5))\n",
    "ax = sns.barplot(x='model', y='response_ratio', data=df_all_results, order=model_response_scores.index, palette='Set2')\n",
    "plt.xticks(rotation=90, ha='right')\n",
    "for p in ax.patches:\n",
    "    ax.annotate(f'{p.get_height():.2f}', (p.get_x() + p.get_width() / 2., p.get_height() - 0.2), \n",
    "                ha='center', va='center', fontsize=10, color='black', \n",
    "                xytext=(0, 5), textcoords='offset points', fontweight='bold')\n",
    "plt.ylabel('Valid response Ratio', fontsize=14, fontweight='bold')\n",
    "plt.xlabel('')\n",
    "plt.xticks(fontsize=12, fontweight='bold')\n",
    "plt.yticks(fontsize=12, fontweight='bold')\n",
    "ax.set_xticklabels([models_short_names[model.get_text()] for model in ax.get_xticklabels()])\n",
    "plt.tight_layout()\n",
    "# save tranparent pdf\n",
    "plt.savefig(f'./results/models_valid_response_ratio.pdf', transparent=True, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f362c77a",
   "metadata": {},
   "source": [
    "## 5.2 Valid Response Rates Per Dataset <a class=\"anchor\" id = \"valid-response-rates-per-dataset\"></a>\n",
    "`Figure 5`: Plotting the `valid response ratio` per dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9ae2d950",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "dataset_scores = df_all_results.groupby('dataset')['response_ratio'].mean().sort_values(ascending=True)\n",
    "df_all_results['dataset'] = pd.Categorical(df_all_results['dataset'], categories=dataset_scores.index, ordered=True)\n",
    "response_ratio = df_all_results.groupby(['dataset', 'model'])['response_ratio'].mean().reset_index()\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "plt.figure(figsize=(15, 5))\n",
    "ax = sns.barplot(x='dataset', y='response_ratio', data=df_all_results, hue='task')\n",
    "plt.xticks(rotation=90)\n",
    "# put the response ratio on top of the bars\n",
    "for p in ax.patches:\n",
    "    ax.annotate(f'{p.get_height():.2f}', (p.get_x() + p.get_width() / 2., p.get_height()-0.2), ha='center', va='center', fontsize=8, color='black', xytext=(0, 5), textcoords='offset points', fontweight='bold')\n",
    "plt.ylabel('Response ratio', fontsize=12, fontweight='bold')\n",
    "plt.xlabel('')\n",
    "plt.xticks(fontsize=12, ha='right', rotation_mode=\"anchor\", fontweight='bold')\n",
    "plt.legend(title='Task')\n",
    "plt.yticks(fontweight='bold')\n",
    "plt.savefig(f'./results/dataset_valid_response_ratio.pdf', transparent=True, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a554bfb9",
   "metadata": {},
   "source": [
    "# 6. Properties Analysis <a class=\"anchor\" id=\"properties-analysis\"></a>\n",
    "Correlation scores for those properties with exclusively graded judgements across datasets: `Figure 3`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "51133a20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from results.utils.get_results_dataframes import get_model_per_category_score\n",
    "selected_df_all_results[\"category\"] = selected_df_all_results[\"subtask\"].map(\n",
    "    subtask_to_cat\n",
    ")\n",
    "selected_df_cat_results = selected_df_all_results[selected_df_all_results[\"category\"] != \"remove\"]\n",
    "selected_df_cat_results = selected_df_cat_results[selected_df_cat_results[\"category\"] != \"grammaticality\"]\n",
    "\n",
    "graded_models_per_category_score = get_model_per_category_score(\n",
    "    selected_df_cat_results,\n",
    ")\n",
    "graded_models_per_category_score.to_latex(\n",
    "    f\"results/models_per_category_score.tex\", index=False, column_format=\"llllllll|cc\"\n",
    ")\n",
    "selected_df_cat_results = selected_df_cat_results[\n",
    "            selected_df_cat_results[\"type\"] != \"categorical\"\n",
    "        ]\n",
    "selected_df_cat_results = selected_df_cat_results.sort_values(by=\"value\")\n",
    "selected_df_cat_results = selected_df_cat_results.sort_values(by=\"category\")\n",
    "selected_df_cat_results[\"model\"] = selected_df_cat_results[\"model\"].map(models_short_names)\n",
    "\n",
    "plt.figure(figsize=(7, 5))\n",
    "sns.barplot(\n",
    "    hue=\"model\",\n",
    "    y=\"value\",\n",
    "    data=selected_df_cat_results,\n",
    "    x=\"category\",\n",
    ")\n",
    "plt.xticks(\n",
    "    [0.8, 1.6, 2.7, 3.5, 4.4, 5.5, 6.5],\n",
    "    labels=[\n",
    "        \"acceptability\",\n",
    "        \"coherence\",\n",
    "        \"consistency\",\n",
    "        \"engaging\",\n",
    "        \"fluency\",\n",
    "        \"relevance\",\n",
    "        \"verbosity\",\n",
    "    ],\n",
    "    rotation=15,\n",
    "    fontsize=12,\n",
    "    ha=\"right\",\n",
    "    rotation_mode=\"anchor\",\n",
    ")\n",
    "plt.legend(loc=\"best\", fontsize=10, ncol=2)\n",
    "plt.ylabel(\"Spearman Correlation\", fontsize=12, fontweight=\"bold\")\n",
    "plt.xlabel(\"\")\n",
    "plt.savefig(\n",
    "    f\"./results/across_task_categories.pdf\", transparent=True, bbox_inches=\"tight\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36cd44bd",
   "metadata": {},
   "source": [
    "# 7. All Model Results (Table 3) <a class=\"anchor\" id=\"all-model-results\"></a>\n",
    "`Table 3`: All Models performance per dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2ea28c80",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "all datasets converted – use a spreadsheet app to open ./results/eval_results.xlsx!\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Type</th>\n",
       "      <th>Dataset (\\#Subtasks)</th>\n",
       "      <th>GPT-4o</th>\n",
       "      <th>Llama-3-70B</th>\n",
       "      <th>Mixtral-8x22B</th>\n",
       "      <th>Gemini-1.5</th>\n",
       "      <th>Mixtral-8x7B</th>\n",
       "      <th>Comm-R+</th>\n",
       "      <th>Comm-R4</th>\n",
       "      <th>Llama-3-8B</th>\n",
       "      <th>Mistral-7B</th>\n",
       "      <th>Starling-7B</th>\n",
       "      <th>OLMo-7B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{blue!25}cola (1)</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{blue!25}llmbar-natural (1)</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{blue!25}llmbar-adversarial (1)</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.11</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{blue!25}toxic-chat (2)</td>\n",
       "      <td>0.49 ±0.36</td>\n",
       "      <td>0.4 ±0.13</td>\n",
       "      <td>0.45 ±0.27</td>\n",
       "      <td>0.45 ±0.35</td>\n",
       "      <td>0.36 ±0.12</td>\n",
       "      <td>0.28 ±0.35</td>\n",
       "      <td>0.2 ±0.21</td>\n",
       "      <td>0.4 ±0.27</td>\n",
       "      <td>0.4 ±0.14</td>\n",
       "      <td>0.27 ±0.26</td>\n",
       "      <td>-0.0 ±0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{blue!25}cola-grammar (63)</td>\n",
       "      <td>0.47 ±0.22</td>\n",
       "      <td>0.29 ±0.24</td>\n",
       "      <td>0.28 ±0.23</td>\n",
       "      <td>0.26 ±0.24</td>\n",
       "      <td>0.21 ±0.18</td>\n",
       "      <td>0.13 ±0.14</td>\n",
       "      <td>0.08 ±0.1</td>\n",
       "      <td>0.17 ±0.18</td>\n",
       "      <td>0.06 ±0.08</td>\n",
       "      <td>0.07 ±0.08</td>\n",
       "      <td>0.01 ±0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{red!25}topical-chat (2)</td>\n",
       "      <td>0.05 ±0.07</td>\n",
       "      <td>-0.02 ±0.02</td>\n",
       "      <td>-0.03 ±0.04</td>\n",
       "      <td>-0.03 ±0.04</td>\n",
       "      <td>0.02 ±0.03</td>\n",
       "      <td>0.01 ±0.02</td>\n",
       "      <td>0.01 ±0.01</td>\n",
       "      <td>0.69 ±0.44</td>\n",
       "      <td>0.06 ±0.08</td>\n",
       "      <td>0.04 ±0.06</td>\n",
       "      <td>0.03 ±0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{red!25}roscoe-gsm8k (2)</td>\n",
       "      <td>0.59 ±0.35</td>\n",
       "      <td>0.65 ±0.27</td>\n",
       "      <td>0.62 ±0.38</td>\n",
       "      <td>0.6 ±0.24</td>\n",
       "      <td>0.58 ±0.36</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.21 ±0.03</td>\n",
       "      <td>0.18 ±0.24</td>\n",
       "      <td>0.56 ±0.4</td>\n",
       "      <td>-0.03 ±0.01</td>\n",
       "      <td>0.06 ±0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{red!25}roscoe-esnli (2)</td>\n",
       "      <td>0.29 ±0.06</td>\n",
       "      <td>0.31 ±0.15</td>\n",
       "      <td>0.13 ±0.13</td>\n",
       "      <td>0.11 ±0.18</td>\n",
       "      <td>0.1 ±0.11</td>\n",
       "      <td>0.03 ±0.05</td>\n",
       "      <td>-0.01 ±0.01</td>\n",
       "      <td>0.02 ±0.04</td>\n",
       "      <td>0.11 ±0.02</td>\n",
       "      <td>0.01 ±0.07</td>\n",
       "      <td>0.03 ±0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{red!25}dices-990 (1)</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{red!25}inferential-strategies (1)</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.06</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{red!25}roscoe-cosmos (2)</td>\n",
       "      <td>0.16 ±0.07</td>\n",
       "      <td>0.14 ±0.09</td>\n",
       "      <td>0.09 ±0.17</td>\n",
       "      <td>0.14 ±0.17</td>\n",
       "      <td>0.19 ±0.05</td>\n",
       "      <td>-0.03 ±0.01</td>\n",
       "      <td>-0.01 ±0.02</td>\n",
       "      <td>0.07 ±0.09</td>\n",
       "      <td>0.21 ±0.07</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{red!25}qags (1)</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{red!25}medical-safety (2)</td>\n",
       "      <td>0.01 ±0.03</td>\n",
       "      <td>-0.02 ±0.03</td>\n",
       "      <td>-0.02 ±0.09</td>\n",
       "      <td>-0.03 ±0.08</td>\n",
       "      <td>0.0 ±0.06</td>\n",
       "      <td>0.01 ±0.02</td>\n",
       "      <td>0.01 ±0.01</td>\n",
       "      <td>-0.0 ±0.01</td>\n",
       "      <td>-0.02 ±0.07</td>\n",
       "      <td>0.0 ±0.02</td>\n",
       "      <td>0.04 ±0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{red!25}dices-350-expert (1)</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{red!25}dices-350-crowdsourced (1)</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{red!25}persona-chat (2)</td>\n",
       "      <td>0.24 ±0.34</td>\n",
       "      <td>0.15 ±0.21</td>\n",
       "      <td>0.58 ±0.59</td>\n",
       "      <td>-0.03 ±0.04</td>\n",
       "      <td>0.54 ±0.65</td>\n",
       "      <td>0.48 ±0.74</td>\n",
       "      <td>0.01 ±0.01</td>\n",
       "      <td>0.47 ±0.74</td>\n",
       "      <td>0.0 ±0.01</td>\n",
       "      <td>-0.03 ±0.04</td>\n",
       "      <td>0.03 ±0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{red!25}roscoe-drop (2)</td>\n",
       "      <td>0.29 ±0.08</td>\n",
       "      <td>0.18 ±0.09</td>\n",
       "      <td>0.2 ±0.12</td>\n",
       "      <td>0.08 ±0.05</td>\n",
       "      <td>0.13 ±0.21</td>\n",
       "      <td>0.03 ±0.04</td>\n",
       "      <td>0.02 ±0.07</td>\n",
       "      <td>0.01 ±0.03</td>\n",
       "      <td>0.08 ±0.01</td>\n",
       "      <td>0.01 ±0.03</td>\n",
       "      <td>-0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{blue!25}dailydialog (1)</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{blue!25}switchboard (1)</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{red!25}persona-chat (4)</td>\n",
       "      <td>0.22 ±0.11</td>\n",
       "      <td>0.03 ±0.11</td>\n",
       "      <td>0.16 ±0.1</td>\n",
       "      <td>0.1 ±0.09</td>\n",
       "      <td>0.02 ±0.15</td>\n",
       "      <td>0.07 ±0.13</td>\n",
       "      <td>0.05 ±0.2</td>\n",
       "      <td>0.01 ±0.15</td>\n",
       "      <td>-0.08 ±0.12</td>\n",
       "      <td>0.03 ±0.13</td>\n",
       "      <td>0.04 ±0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{red!25}topical-chat (4)</td>\n",
       "      <td>0.26 ±0.03</td>\n",
       "      <td>0.21 ±0.15</td>\n",
       "      <td>0.13 ±0.04</td>\n",
       "      <td>0.17 ±0.12</td>\n",
       "      <td>0.21 ±0.18</td>\n",
       "      <td>0.14 ±0.05</td>\n",
       "      <td>0.07 ±0.07</td>\n",
       "      <td>0.19 ±0.11</td>\n",
       "      <td>0.25 ±0.21</td>\n",
       "      <td>0.14 ±0.16</td>\n",
       "      <td>-0.04 ±0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{red!25}recipe-crowd-sourcing-data (6)</td>\n",
       "      <td>0.78 ±0.05</td>\n",
       "      <td>0.75 ±0.05</td>\n",
       "      <td>0.6 ±0.15</td>\n",
       "      <td>0.67 ±0.09</td>\n",
       "      <td>0.57 ±0.24</td>\n",
       "      <td>0.32 ±0.28</td>\n",
       "      <td>0.06 ±0.26</td>\n",
       "      <td>0.38 ±0.17</td>\n",
       "      <td>0.5 ±0.14</td>\n",
       "      <td>0.04 ±0.17</td>\n",
       "      <td>-0.15 ±0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{red!25}roscoe-cosmos (2)</td>\n",
       "      <td>0.57 ±0.18</td>\n",
       "      <td>0.52 ±0.16</td>\n",
       "      <td>0.51 ±0.16</td>\n",
       "      <td>0.57 ±0.17</td>\n",
       "      <td>0.53 ±0.21</td>\n",
       "      <td>0.33 ±0.25</td>\n",
       "      <td>0.48 ±0.17</td>\n",
       "      <td>0.14 ±0.17</td>\n",
       "      <td>0.56 ±0.24</td>\n",
       "      <td>0.13 ±0.04</td>\n",
       "      <td>0.13 ±0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{red!25}roscoe-drop (2)</td>\n",
       "      <td>0.57 ±0.22</td>\n",
       "      <td>0.5 ±0.17</td>\n",
       "      <td>0.44 ±0.15</td>\n",
       "      <td>0.44 ±0.13</td>\n",
       "      <td>0.32 ±0.12</td>\n",
       "      <td>0.21 ±0.22</td>\n",
       "      <td>0.37 ±0.18</td>\n",
       "      <td>0.22 ±0.11</td>\n",
       "      <td>0.36 ±0.16</td>\n",
       "      <td>0.16 ±0.17</td>\n",
       "      <td>0.06 ±0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{red!25}roscoe-esnli (2)</td>\n",
       "      <td>0.49 ±0.24</td>\n",
       "      <td>0.38 ±0.21</td>\n",
       "      <td>0.38 ±0.17</td>\n",
       "      <td>0.35 ±0.21</td>\n",
       "      <td>0.32 ±0.12</td>\n",
       "      <td>0.09 ±0.08</td>\n",
       "      <td>0.28 ±0.21</td>\n",
       "      <td>-0.07 ±0.15</td>\n",
       "      <td>0.23 ±0.23</td>\n",
       "      <td>0.11 ±0.06</td>\n",
       "      <td>-0.01 ±0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{red!25}roscoe-gsm8k (2)</td>\n",
       "      <td>0.82 ±0.12</td>\n",
       "      <td>0.77 ±0.17</td>\n",
       "      <td>0.81 ±0.14</td>\n",
       "      <td>0.81 ±0.12</td>\n",
       "      <td>0.79 ±0.13</td>\n",
       "      <td>0.68 ±0.2</td>\n",
       "      <td>0.7 ±0.08</td>\n",
       "      <td>0.34 ±0.06</td>\n",
       "      <td>0.43 ±0.11</td>\n",
       "      <td>0.46 ±0.13</td>\n",
       "      <td>-0.14 ±0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{red!25}newsroom (4)</td>\n",
       "      <td>0.59 ±0.02</td>\n",
       "      <td>0.63 ±0.01</td>\n",
       "      <td>0.44 ±0.05</td>\n",
       "      <td>0.55 ±0.03</td>\n",
       "      <td>0.5 ±0.07</td>\n",
       "      <td>0.36 ±0.06</td>\n",
       "      <td>0.16 ±0.05</td>\n",
       "      <td>0.46 ±0.12</td>\n",
       "      <td>0.31 ±0.05</td>\n",
       "      <td>0.21 ±0.08</td>\n",
       "      <td>-0.01 ±0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{red!25}summeval (4)</td>\n",
       "      <td>0.35 ±0.06</td>\n",
       "      <td>0.25 ±0.16</td>\n",
       "      <td>0.54 ±0.08</td>\n",
       "      <td>0.38 ±0.02</td>\n",
       "      <td>0.48 ±0.02</td>\n",
       "      <td>0.19 ±0.06</td>\n",
       "      <td>0.13 ±0.06</td>\n",
       "      <td>0.21 ±0.13</td>\n",
       "      <td>0.37 ±0.06</td>\n",
       "      <td>0.15 ±0.05</td>\n",
       "      <td>-0.0 ±0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{red!25}wmt-23-en-de (1)</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.16</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{red!25}wmt-23-zh-en (1)</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{red!25}wmt-human-en-de (1)</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.15</td>\n",
       "      <td>-0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td></td>\n",
       "      <td>\\cellcolor{red!25}wmt-human-zh-en (1)</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.15</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Type                              Dataset (\\#Subtasks)      GPT-4o  \\\n",
       "0                             \\cellcolor{blue!25}cola (1)       0.34    \n",
       "1                   \\cellcolor{blue!25}llmbar-natural (1)       0.84    \n",
       "2               \\cellcolor{blue!25}llmbar-adversarial (1)       0.58    \n",
       "3                       \\cellcolor{blue!25}toxic-chat (2)  0.49 ±0.36   \n",
       "4                    \\cellcolor{blue!25}cola-grammar (63)  0.47 ±0.22   \n",
       "5                      \\cellcolor{red!25}topical-chat (2)  0.05 ±0.07   \n",
       "6                      \\cellcolor{red!25}roscoe-gsm8k (2)  0.59 ±0.35   \n",
       "7                      \\cellcolor{red!25}roscoe-esnli (2)  0.29 ±0.06   \n",
       "8                         \\cellcolor{red!25}dices-990 (1)      -0.24    \n",
       "9            \\cellcolor{red!25}inferential-strategies (1)       0.42    \n",
       "10                    \\cellcolor{red!25}roscoe-cosmos (2)  0.16 ±0.07   \n",
       "11                             \\cellcolor{red!25}qags (1)       0.72    \n",
       "12                   \\cellcolor{red!25}medical-safety (2)  0.01 ±0.03   \n",
       "13                 \\cellcolor{red!25}dices-350-expert (1)       -0.2    \n",
       "14           \\cellcolor{red!25}dices-350-crowdsourced (1)      -0.22    \n",
       "15                     \\cellcolor{red!25}persona-chat (2)  0.24 ±0.34   \n",
       "16                      \\cellcolor{red!25}roscoe-drop (2)  0.29 ±0.08   \n",
       "0                      \\cellcolor{blue!25}dailydialog (1)       0.69    \n",
       "1                      \\cellcolor{blue!25}switchboard (1)       0.66    \n",
       "2                      \\cellcolor{red!25}persona-chat (4)  0.22 ±0.11   \n",
       "3                      \\cellcolor{red!25}topical-chat (4)  0.26 ±0.03   \n",
       "4        \\cellcolor{red!25}recipe-crowd-sourcing-data (6)  0.78 ±0.05   \n",
       "5                     \\cellcolor{red!25}roscoe-cosmos (2)  0.57 ±0.18   \n",
       "6                       \\cellcolor{red!25}roscoe-drop (2)  0.57 ±0.22   \n",
       "7                      \\cellcolor{red!25}roscoe-esnli (2)  0.49 ±0.24   \n",
       "8                      \\cellcolor{red!25}roscoe-gsm8k (2)  0.82 ±0.12   \n",
       "9                          \\cellcolor{red!25}newsroom (4)  0.59 ±0.02   \n",
       "10                         \\cellcolor{red!25}summeval (4)  0.35 ±0.06   \n",
       "11                     \\cellcolor{red!25}wmt-23-en-de (1)       0.22    \n",
       "12                     \\cellcolor{red!25}wmt-23-zh-en (1)       0.17    \n",
       "13                  \\cellcolor{red!25}wmt-human-en-de (1)       0.63    \n",
       "14                  \\cellcolor{red!25}wmt-human-zh-en (1)       0.54    \n",
       "\n",
       "    Llama-3-70B Mixtral-8x22B   Gemini-1.5 Mixtral-8x7B      Comm-R+  \\\n",
       "0         0.56          0.54         0.45         0.55         0.12    \n",
       "1         0.72          0.72         0.79         0.54         0.56    \n",
       "2         0.41           0.2         0.29         0.06         0.11    \n",
       "3     0.4 ±0.13    0.45 ±0.27   0.45 ±0.35   0.36 ±0.12   0.28 ±0.35   \n",
       "4    0.29 ±0.24    0.28 ±0.23   0.26 ±0.24   0.21 ±0.18   0.13 ±0.14   \n",
       "5   -0.02 ±0.02   -0.03 ±0.04  -0.03 ±0.04   0.02 ±0.03   0.01 ±0.02   \n",
       "6    0.65 ±0.27    0.62 ±0.38    0.6 ±0.24   0.58 ±0.36          0.0   \n",
       "7    0.31 ±0.15    0.13 ±0.13   0.11 ±0.18    0.1 ±0.11   0.03 ±0.05   \n",
       "8        -0.16         -0.16        -0.12         -0.2        -0.09    \n",
       "9         0.08          0.02         0.22         0.06        -0.02    \n",
       "10   0.14 ±0.09    0.09 ±0.17   0.14 ±0.17   0.19 ±0.05  -0.03 ±0.01   \n",
       "11        0.69          0.66         0.65         0.68         0.13    \n",
       "12  -0.02 ±0.03   -0.02 ±0.09  -0.03 ±0.08    0.0 ±0.06   0.01 ±0.02   \n",
       "13        -0.2         -0.15        -0.03        -0.11         0.01    \n",
       "14       -0.15         -0.08        -0.02        -0.11        -0.08    \n",
       "15   0.15 ±0.21    0.58 ±0.59  -0.03 ±0.04   0.54 ±0.65   0.48 ±0.74   \n",
       "16   0.18 ±0.09     0.2 ±0.12   0.08 ±0.05   0.13 ±0.21   0.03 ±0.04   \n",
       "0          0.6          0.55         0.63         0.63         0.52    \n",
       "1          0.6          0.63         0.59         0.56         0.36    \n",
       "2    0.03 ±0.11     0.16 ±0.1    0.1 ±0.09   0.02 ±0.15   0.07 ±0.13   \n",
       "3    0.21 ±0.15    0.13 ±0.04   0.17 ±0.12   0.21 ±0.18   0.14 ±0.05   \n",
       "4    0.75 ±0.05     0.6 ±0.15   0.67 ±0.09   0.57 ±0.24   0.32 ±0.28   \n",
       "5    0.52 ±0.16    0.51 ±0.16   0.57 ±0.17   0.53 ±0.21   0.33 ±0.25   \n",
       "6     0.5 ±0.17    0.44 ±0.15   0.44 ±0.13   0.32 ±0.12   0.21 ±0.22   \n",
       "7    0.38 ±0.21    0.38 ±0.17   0.35 ±0.21   0.32 ±0.12   0.09 ±0.08   \n",
       "8    0.77 ±0.17    0.81 ±0.14   0.81 ±0.12   0.79 ±0.13    0.68 ±0.2   \n",
       "9    0.63 ±0.01    0.44 ±0.05   0.55 ±0.03    0.5 ±0.07   0.36 ±0.06   \n",
       "10   0.25 ±0.16    0.54 ±0.08   0.38 ±0.02   0.48 ±0.02   0.19 ±0.06   \n",
       "11        0.19          0.23         0.16         0.17         0.22    \n",
       "12        0.15          0.19         0.14         0.15         0.15    \n",
       "13        0.37          0.51         0.46          0.2         0.42    \n",
       "14        0.37          0.48         0.41         0.25         0.42    \n",
       "\n",
       "        Comm-R4   Llama-3-8B   Mistral-7B  Starling-7B      OLMo-7B  \n",
       "0         0.01         0.49         0.35         0.45         0.08   \n",
       "1         0.59         0.52         0.39         0.28         0.06   \n",
       "2         -0.2        -0.17        -0.06        -0.12         0.12   \n",
       "3     0.2 ±0.21    0.4 ±0.27    0.4 ±0.14   0.27 ±0.26   -0.0 ±0.03  \n",
       "4     0.08 ±0.1   0.17 ±0.18   0.06 ±0.08   0.07 ±0.08   0.01 ±0.02  \n",
       "5    0.01 ±0.01   0.69 ±0.44   0.06 ±0.08   0.04 ±0.06   0.03 ±0.04  \n",
       "6    0.21 ±0.03   0.18 ±0.24    0.56 ±0.4  -0.03 ±0.01   0.06 ±0.11  \n",
       "7   -0.01 ±0.01   0.02 ±0.04   0.11 ±0.02   0.01 ±0.07   0.03 ±0.02  \n",
       "8        -0.02        -0.12         -0.0        -0.05        -0.01   \n",
       "9        -0.12        -0.01        -0.02         0.01         0.02   \n",
       "10  -0.01 ±0.02   0.07 ±0.09   0.21 ±0.07         0.03        -0.04  \n",
       "11        0.33         0.52         0.21         0.02         0.33   \n",
       "12   0.01 ±0.01   -0.0 ±0.01  -0.02 ±0.07    0.0 ±0.02   0.04 ±0.01  \n",
       "13        0.01        -0.06         0.01         0.01        -0.02   \n",
       "14        0.01        -0.07        -0.01         0.01         0.04   \n",
       "15   0.01 ±0.01   0.47 ±0.74    0.0 ±0.01  -0.03 ±0.04   0.03 ±0.04  \n",
       "16   0.02 ±0.07   0.01 ±0.03   0.08 ±0.01   0.01 ±0.03        -0.05  \n",
       "0         0.23         0.59         0.36         0.09         0.04   \n",
       "1         0.53         0.34          0.4         0.13          0.0   \n",
       "2     0.05 ±0.2   0.01 ±0.15  -0.08 ±0.12   0.03 ±0.13   0.04 ±0.14  \n",
       "3    0.07 ±0.07   0.19 ±0.11   0.25 ±0.21   0.14 ±0.16  -0.04 ±0.04  \n",
       "4    0.06 ±0.26   0.38 ±0.17    0.5 ±0.14   0.04 ±0.17  -0.15 ±0.15  \n",
       "5    0.48 ±0.17   0.14 ±0.17   0.56 ±0.24   0.13 ±0.04   0.13 ±0.07  \n",
       "6    0.37 ±0.18   0.22 ±0.11   0.36 ±0.16   0.16 ±0.17   0.06 ±0.09  \n",
       "7    0.28 ±0.21  -0.07 ±0.15   0.23 ±0.23   0.11 ±0.06  -0.01 ±0.12  \n",
       "8     0.7 ±0.08   0.34 ±0.06   0.43 ±0.11   0.46 ±0.13  -0.14 ±0.03  \n",
       "9    0.16 ±0.05   0.46 ±0.12   0.31 ±0.05   0.21 ±0.08  -0.01 ±0.05  \n",
       "10   0.13 ±0.06   0.21 ±0.13   0.37 ±0.06   0.15 ±0.05   -0.0 ±0.03  \n",
       "11        0.19         0.13         0.16        -0.09         0.03   \n",
       "12        0.14         0.08         0.14         0.01        -0.03   \n",
       "13        0.15         0.05         0.31         0.15        -0.06   \n",
       "14        0.15         0.12         0.37         0.15         -0.0   "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from results.utils.generate_tables import get_all_models_per_dataset_score\n",
    "df_all_results = pd.DataFrame(all_results)\n",
    "print(f'\\nall datasets converted – use a spreadsheet app to open {xlsx_fpath}!')\n",
    "# remove gpt3 \n",
    "df_all_results = df_all_results[~df_all_results['model'].str.contains('gpt-3.5')]\n",
    "categorical_models_per_dataset_score = get_all_models_per_dataset_score(\n",
    "    df_all_results, type=\"categorical\"\n",
    ")\n",
    "graded_models_per_dataset_score = get_all_models_per_dataset_score(\n",
    "    df_all_results, type=\"graded\"\n",
    ")\n",
    "# combine the two dataframes\n",
    "models_per_dataset_score = pd.concat(\n",
    "    [categorical_models_per_dataset_score, graded_models_per_dataset_score], axis=0\n",
    ")\n",
    "models_per_dataset_score = models_per_dataset_score.rename(\n",
    "    columns={\"Dataset (#Subtasks)\": \"Dataset (\\#Subtasks)\"}\n",
    ")\n",
    "models_per_dataset_score.to_latex(\n",
    "    f\"results/all_models_per_dataset_score.tex\",\n",
    "    index=False,\n",
    "    column_format=\"lllllllllllll\",\n",
    ")\n",
    "models_per_dataset_score"
   ]
  }
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